Clarifyprompt MCP Server

ClarifyPrompt MCP

A context-aware MCP prompt compiler that transforms vague prompts into platform-optimized prompts for 60+ AI platforms across 7 categories — grounded in your workspace signals (CLAUDE.md, AGENTS.md, .cursorrules, package.json), resolved intent, and the capabilities of the target model.

Send a raw prompt. ClarifyPrompt gathers the right context, resolves what you're actually trying to do, and returns a version specifically optimized for Midjourney, DALL-E, Sora, Runway, Higgsfield, ElevenLabs, Claude, ChatGPT, Cursor, or any of the 60+ supported platforms — with the right syntax, parameters, structure, and grounding.

New in 1.12.0: Roadmap step #7 — ClarifyPrompt now speaks A2A (Agent-to-Agent). Set CLARIFYPROMPT_TRANSPORT=a2a and it serves as a discoverable A2A peer: an agent card at /.well-known/agent-card.json, a compile-prompt-for-platform skill over JSON-RPC (message/send) with live SSE streaming (message/stream), and first-class task cancellation + input-required clarification round-trips — all powered by the same compose pipeline. Other agents can now call ClarifyPrompt to compile prompts. stdio stays the default; nothing about existing setups changes. See CHANGELOG.md.

How It Works

You write:    "a dragon flying over a castle at sunset"

ClarifyPrompt returns (for Midjourney):
  "a majestic dragon flying over a medieval castle at sunset
   --ar 16:9 --v 6.1 --style raw --q 2 --chaos 30 --s 700"

ClarifyPrompt returns (for DALL-E):
  "A majestic dragon flying over a castle at sunset. Size: 1024x1024"

Same prompt, different platform, completely different output. ClarifyPrompt knows what each platform expects — and in 1.2.0, it also knows what you're working on.

What's new in 1.12.0

Step #7 — the final step — of the MCP modernization roadmap: ClarifyPrompt now speaks A2A (Agent-to-Agent), so other agents can call it to compile prompts. stdio stays the default; nothing about existing setups changes.

Set CLARIFYPROMPT_TRANSPORT=a2a and ClarifyPrompt comes up as a discoverable A2A peer on Node's built-in http (the only new dependency is the official @a2a-js/sdk, which itself pulls just uuid):

CLARIFYPROMPT_TRANSPORT=a2a CLARIFYPROMPT_HTTP_PORT=3000 npx clarifyprompt-mcp
# → card:  http://127.0.0.1:3000/.well-known/agent-card.json
# → a2a:   POST http://127.0.0.1:3000/a2a   (message/send · message/stream)

The whole roadmap pays off here — one incoming A2A message flows through the same compose pipeline, and the primitives built in earlier steps map straight onto A2A semantics:

• Compile — a message/send with the raw prompt (plain text, or JSON { prompt, platform?, category?, … }) returns a task whose artifact carries the optimized prompt (text) plus the full structured compose result (data).
• Streaming (1.10.0 progress → A2A) — message/stream emits status-update events as each pipeline stage runs, then the artifact, over SSE.
• Cancellation (1.10.0 AbortSignal → A2A) — tasks/cancel aborts the in-flight compose within milliseconds and reports a terminal canceled state.
• Clarification (1.9.0 elicitation → A2A) — clarify is off by default for one-shot peers; opt in with pre_clarify: 'auto' | 'always' and an ambiguous prompt pauses the task in A2A's first-class input-required state with the questions (readable text + structured data). Answer on the same task and it compiles.

Configure the public base URL advertised in the card with CLARIFYPROMPT_A2A_BASE_URL (handy behind a proxy); port/host are shared with streamable-http. New deterministic npm run test:a2a battery drives card discovery, a live compile, the clarify round-trip, and SSE streaming.

What's new in 1.11.0

Step #6 of the MCP modernization roadmap: a pluggable transport factory — ClarifyPrompt can now serve over Streamable HTTP, the runway toward A2A and remote MCP hosts. stdio stays the default; nothing about existing setups changes.

Transports

Set CLARIFYPROMPT_TRANSPORT:

HTTP knobs (in streamable-http / a2a mode): CLARIFYPROMPT_HTTP_PORT (3000), CLARIFYPROMPT_HTTP_HOST (127.0.0.1 — localhost-only by default), CLARIFYPROMPT_HTTP_PATH (/mcp, streamable-http only).

CLARIFYPROMPT_TRANSPORT=streamable-http CLARIFYPROMPT_HTTP_PORT=3000 npx clarifyprompt-mcp
# → POST http://127.0.0.1:3000/mcp  ·  GET http://127.0.0.1:3000/health

Tool/resource registration moved into an exported createServer() factory: stdio gets one server, streamable-http gets one per session (the SDK-recommended, GHSA-safe pattern — never shares a server across HTTP clients). New deterministic npm run test:http battery drives a full HTTP session.

What's new in 1.10.0

Step #5 of the MCP modernization roadmap, stable core: compose_prompt is cancellable and reports live progress. Model-agnostic, opt-in, fully back-compat.

Cancellation

An AbortSignal is plumbed through the entire LLM path (simpleGenerate → chat → fetch, combined with the per-call timeout) and every engine stage. When a client sends notifications/cancelled for a compose_prompt call, the in-flight model request aborts immediately and the revise loop stops at the next stage boundary — instead of running every iteration to completion. The signal reaches fetch regardless of which model/provider is configured.

Progress

Include a progressToken in the compose_prompt request _meta and the server emits notifications/progress at each stage (clarify / optimize / ground / critique) with a monotonic counter and a human message like optimizing prompt [iter 2/3]. Hosts can show a live status on a long multi-iteration compose. No token → no notifications, zero overhead.

Why not MCP tasks (yet)

Roadmap #5 named the MCP tasks API. It's still experimental/ in the SDK ("may change without notice"), its reference is ~600 lines, and no current client speaks the tasks/* protocol — so a full implementation would be unusable off-by-default code today. The real value (cancellable + progress-reporting compose) is delivered here on stable primitives; the experimental async-task wrapper is deferred to land with #7 (A2A), which the AbortSignal groundwork here already sets up. New deterministic npm run test:cancel battery locks the behavior.

What's new in 1.9.0

Step #4 of the MCP modernization roadmap: clarify_with_user can elicit answers through the host's native form UI. Opt-in, fully back-compat.

Interactive clarification

Pass elicit: true. On a client that supports MCP elicitation, the clarifying questions become a real form:

• each question is a field, options become enum dropdowns, and each suggestedAnswer is the field default (one-click accept);
• the user answers inline; the engine returns answers: [{ question, dimension, answer, usedSuggested }] with elicited: true.

Without elicit, on a non-capable client, or if the round-trip errors, the tool returns the same raw-questions JSON it always has — every existing caller is unaffected. decline / cancel are surfaced via elicitationAction.

This turns clarification from "here's a JSON blob of questions, you render it" into a first-class interactive moment in hosts like Claude Desktop. The mapping lives in a small pure module (src/engine/clarification/elicit.ts), reusable by compose_prompt's pre-clarify stage later. New deterministic npm run test:elicit battery (pure helpers + a live mock-client round-trip) locks it.

What's new in 1.8.0

Step #3 of the MCP modernization roadmap: the engine's read surfaces become browseable resource templates with argument autocompletion. No tool or engine behavior changes.

Resource templates

Four templates join the static clarifyprompt://categories, each backed by an existing engine getter:

MCP hosts with a resource browser (Claude Desktop, Cursor) now get a navigable tree instead of a single static blob.

Autocomplete

completion/complete resolves the template variables: {category} → the 7 category ids, {id} → platform ids scoped by the chosen {category}, {date} → days with traces, pack ids, memory scopes. (MCP completion applies to prompt args + resource-template variables only — not tool inputs; ClarifyPrompt registers no prompts, so it lives on the templates.)

Capabilities

The server now advertises resources (with templates) and completions at initialize. New deterministic npm run test:resources battery locks the surface.

What's new in 1.7.1

Patch fixing #3: a silent empty optimized prompt from models whose answer didn't land in content.

• Reads all three thinking-channel field names (reasoning / thinking / reasoning_content) — fixes DeepSeek / qwen-thinking and similar.
• Retries once, then fails loudly when content is empty regardless of any thinking field. This covers the real issue #3 case: gpt-oss harmony output over Ollama's /v1 shim generates tokens (completion_tokens > 0) but returns content: "" with no thinking field. The engine now degrades to the original prompt + a surfaced error instead of returning blank.
• Genuinely recovering gpt-oss harmony output (via Ollama's native /api/chat) is a tracked follow-up — out of scope for a patch. The silent-failure harm is resolved now.
• New deterministic npm run test:thinking battery locks the regression with mocked responses (no live cloud dependency).

Verified: test:thinking, reasoning battery (gpt-oss degrades loudly; the genuine reasoner kimi-k2-thinking:cloud still returns real content), integration, day2, evals, wire.

What's new in 1.7.0

Step #2 of the MCP modernization roadmap: the entire tool surface migrated off the deprecated server.tool() shorthand (removed in SDK 2.0) onto server.registerTool(). No engine behavior changes; full back-compat.

What hosts get

• Titles — every tool has a human-readable display name ("Forget a fact", not memory_forget).
• Behavior annotations — all 23 tools declare readOnlyHint / destructiveHint / idempotentHint / openWorldHint. The three destructive tools (memory_forget, unload_pack, unregister_platform) are flagged for confirmation UIs; the seven read-only inspectors are flagged safe-to-call-freely; the seven tools that reach the network (LLM / embeddings / web search) carry openWorldHint: true.
• Structured output — every tool declares an outputSchema and returns structuredContent alongside the JSON text. Schemas are permissive by design (all-optional, passthrough) — they document the shape without ever rejecting engine output.

Back-compat

Text content is byte-identical for every tool — including the three array-returning list_* tools, whose text stays a bare array while structuredContent wraps it in an object per the MCP spec. Error returns unchanged. Verified: wire 7/7, integration 9/9, day2, 26/27 evals with zero output-validation errors.

Found during verification

#3 — cloud gpt-oss thinking-channel responses can yield an empty optimizedPrompt (remote API change exposing a pre-existing field-name gap in client.ts; fix targeted for 1.7.1).

What's new in 1.6.8

Housekeeping release closing the loops the 1.6.5→1.6.7 cascade opened. No engine code, MCP tool surface, platform, or env-var changes.

Changed

• CI matrix now tests Node 24 (current active LTS, EOL Apr 2028) alongside 18/20/22 across Ubuntu + macOS. The matrix previously tested two EOL Node versions but not the current LTS at all. Verified before merge that the native deps (better-sqlite3 + sqlite-vec) load and function on Node 24.16.0 in a toolchain-free node:24-slim container. engines stays >=18 — maximum compatibility, and we test what we claim.
• Publish runner moved Node 20 → 22, keeping an EOL runtime off the release-critical path (matches the Dockerfile base).

Process

• New ship-check CP-13 — lockfile regeneration safety encodes the lesson from the 1.6.5→1.6.6→1.6.7 cascade: a single npm install --package-lock-only silently dropped 4 of 5 sqlite-vec platform binaries (broke Linux CI) and pulled a within-caret better-sqlite3 bump that dropped Node 20 prebuilds (broke the Docker build). The check mandates full npm install on dep changes, a lockfile diff for dropped platform deps + native-dep version jumps, and a local slim-Docker load gate. Dogfooded on this release.

What's new in 1.6.7

Dockerfile patch. No engine code, MCP tool surface, platform, or env-var changes.

Fixed

• CI / docker build failed on 1.6.6 with npm error gyp ERR! find Python. Root cause: better-sqlite3@12.10.0 (released 2026-05) explicitly removed prebuilt binaries for Node.js v20 and v23 because Node 20 reached EOL in April 2026. The 1.6.6 lockfile regen pulled 12.10.0 within the ^12.9.0 caret, and node:20-slim doesn't have Python + a C++ toolchain to compile from source. Bumped the Dockerfile base to node:22-slim — current active LTS, still has working prebuilts.
• The non-Docker CI build matrix (Node 18 / 20 / 22 across macOS + Ubuntu) still passes because regular runners can compile-from-source as fallback. Only the slim Docker image stumbles.

Verified locally

docker build → green. Container can require('better-sqlite3') + require('sqlite-vec') cleanly. All 5 sqlite-vec platform binaries still in package-lock.json (1.6.6's fix held).

What's new in 1.6.6

Lockfile + harness patch following 1.6.5. No engine code, MCP tool surface, platform, or env-var changes. Ships the MCP-completeness audit doc.

Fixed

• package-lock.json lost 4 of 5 sqlite-vec platform binaries during the 1.6.5 SDK bump. My local npm install --package-lock-only retained only the maintainer's sqlite-vec-darwin-arm64 binary. npm ci on CI's Ubuntu runners failed with no such module: vec0 because sqlite-vec-linux-x64 wasn't in the lock. End-user npm install clarifyprompt-mcp@1.6.5 was never affected (the npm tarball doesn't ship a lockfile; users resolve platforms at install time). Regenerated with full npm install so all 5 platforms (darwin-arm64, darwin-x64, linux-arm64, linux-x64, windows-x64) are back.
• Eval harness HTML report writer crashed on ERRORED entries (evals/run.mjs:729). The pre-existing renderer assumed every non-skipped, non-filtered run had an evaluation.checks field, but errored runs carry an error field instead. Added an explicit errored-status branch — the harness now degrades gracefully and exits cleanly even when fixtures error.

Bundled docs

• docs/audits/mcp-completeness-2026-05.md — diagnostic audit of the engine's MCP surface against the current SDK + spec. Tool-by-tool registration table, resource gap analysis, SDK feature delta (1.12 → 1.29 → 2.0-alpha), capability declarations, transport refactor sketch, A2A feasibility note, and a sequenced 7-step modernization roadmap. The artifact behind next-session planning. No engine changes prescribed inline.

Numbers

• 5 sqlite-vec platforms in lockfile (was 1). npm audit --production: 0 vulnerabilities (unchanged). Tools: 23 (unchanged). Eval fixtures: 30 (unchanged).

What's new in 1.6.5

Security patch. No engine code changes, no MCP tool surface changes, no platform changes, no env-var changes.

Fixed

• CVE-2026-0621 — ReDoS in @modelcontextprotocol/sdk's UriTemplate regex (patched in SDK 1.25.2). The previous ^1.12.1 floor allowed vulnerable resolutions on stale npm caches; bumped to ^1.29.0 so the floor itself is patched.
• GHSA-345p-7cg4-v4c7 — Shared server/transport instances leak cross-client response data (patched in SDK 1.26.0). Not exploitable in practice for ClarifyPrompt (one host = one server instance) but the vulnerable code is now out of the dependency graph entirely.
• 7 transitive vulnerabilities (2 moderate, 5 high) in the SDK's bundled HTTP-transport substack (hono, express-rate-limit, fast-uri, ip-address, path-to-regexp, qs, @hono/node-server). Cleared via npm audit fix. Never affected runtime — ClarifyPrompt is stdio-only and doesn't load the HTTP transport — but they were noise in users' npm audit reports and made the install look unsafe.

Numbers

• npm audit --production → 0 vulnerabilities (was 2 SDK CVEs + 7 transitive).
• package-lock.json: net −336 lines (the old caret was pulling in heavy unused HTTP-transport ancillaries; the fix swapped them for slimmer alternates).
• Tools: 23 (unchanged). Platforms: 60+ (unchanged). Eval fixtures: 30 (unchanged).
• Wire test + integration battery + day2 + reasoning + 29/30 evals pass against the new floor on local Ollama. The one eval fail (analyzer-creative-media) is a pre-existing qwen-coder-7b classifier flake — verified SDK-independent by stash-reverting and re-running.

Why the floor bump matters

^1.12.1 was misleading documentation — caret resolution was actually pulling SDK 1.27.1 for any fresh npm install since early 2026. The floor bump aligns the declared baseline with what npm was already doing for most users while guaranteeing the floor for users on stale caches. It also positions us for the eventual 2.0.0-alpha migration when that line stabilizes (the modern SDK deprecates .tool() / .prompt() / .resource() shorthand registration in favor of registerTool() / registerPrompt() / registerResource() with title + outputSchema + annotations).

What's new in 1.6.4

Docs + process patch. No engine, MCP tool, or platform changes — but a meaningful cleanup of the pack-distribution model.

Pack registry consolidated back into the engine repo

LumabyteCo/clarifyprompt-packs (the separate community-pack registry created in 1.3 with the right principle but at the wrong scale) has been archived. Its three starter packs already lived in this repo's packs/ folder; the registry was meant to be the canonical home but in practice everything always shipped from here via the npm tarball. The drift caught up: higgsfield-creative-handbook shipped in 1.6.2 and never made it to the registry, even though the registry's own README told users to fetch packs from there.

Net result of 1.6.4:

• Single source of truth. packs/*.md knowledge packs + packs/platforms/*.yaml platform configs all live in clarifyprompt-mcp and ship in the npm tarball.
• New top-level Knowledge packs section in this README explains the loading model (load_knowledge_pack({source: "<url-or-path>", scope: ...})), the three starter packs + Higgsfield, the scope semantics, and how to contribute.
• New packs/README.md — pack authoring guide (frontmatter schema, chunk boundaries, quality bar). Lifted from the archived registry so the content isn't lost.
• Tombstone redirect on the archived repo. Anyone visiting clarifyprompt-packs lands on a banner pointing here.

When does the split come back?

When there's a forcing function: a community PR queue on packs alone, pack count >20, or divergent licensing/governance. Until then the maintenance cost of keeping two repos in sync wasn't paying for an audience that hadn't materialized.

Numbers

• Tools: 23 (unchanged).
• Platforms: 60+ (unchanged).
• Bundled knowledge packs: 4 (anthropic-brand-voice, higgsfield-creative-handbook, nextjs-14-best-practices, sox-compliance) — same as 1.6.2/1.6.3, just newly canonical.
• Eval fixtures: 30 (unchanged).
• Tarball size: unchanged from 1.6.3.

What's new in 1.6.3

Patch. The 1.6.2 CI tag-push run surfaced two real issues — fixed here without changing any engine code.

Fixed

• evals/fixtures/28-context-includes-git-state.yaml previously asserted git_branch_present: true, but GitHub Actions checks out in detached-HEAD mode where bundle.git.branch is correctly undefined (only the SHA + recent commits are populated). Relaxed to assert bundle_has_git: true only — that's what's actually invariant across local + CI environments.
• evals/fixtures/17-critique-strong-prompt-accepts.yaml asserted verdict: accept + overall_score_min: 7 on a strong prompt. gpt-4o-mini's judge calibrates stricter than qwen2.5-coder:7b's, and occasionally returned a malformed overall field that the parser defaulted to 0 → verdict=reject. The fixture's real intent is to verify engine wiring (5+ dimensions, the standard dimension names present, no harness error) — not to compare judge calibration across models. Dropped the verdict + tight score assertions; kept the wiring-level checks.
• README Glama badge swapped from inline <img> (sometimes broken via GitHub's camo proxy) to a shields.io text-link badge that's stable across all rendering surfaces.

Notes

• No engine code changes. No new MCP tools (still 23). No platform changes (still 60+). No env-var changes.
• Eval baselines unchanged on local Ollama. This is a CI-specific hardening — local runs against qwen-coder-7b produced the same results before and after.
• The CI publish-gate failure that appeared on the v1.6.2 tag push was downstream of the eval failure (Wait for evals workflow step blocked publish). Now that the underlying fixtures don't false-fail on gpt-4o-mini + detached-HEAD CI, the publish gate clears too.

What's new in 1.6.2

Patch. Two additive ships, both no-code-changes from the engine's perspective:

Higgsfield creative-handbook knowledge pack

packs/higgsfield-creative-handbook.md — a community-style markdown pack documenting Higgsfield's actual conventions: model-selection rules (which of the 13 models for which use case), Soul ID character-training workflow, camera-move vocabulary, prompt-structure pattern (long-form prose, not keyword tags), multi-reference editing, Marketing Studio modes, common pitfalls (don't translate Midjourney flags verbatim), output specs.

Load it explicitly:

load_knowledge_pack source="https://raw.githubusercontent.com/LumabyteCo/clarifyprompt-mcp/main/packs/higgsfield-creative-handbook.md"

…or, since it ships in the npm tarball, point at the installed copy. The Context Curator grounds Higgsfield-targeted prompts in this pack's chunks automatically via semantic retrieval. See the Knowledge packs section for the full loading + scoping model.

npm run matrix — multi-model eval matrix runner

evals/matrix.mjs runs npm run eval sequentially against N models and stitches the results into one side-by-side HTML (evals/matrix.html by default). Lights up the model-class-gated fixtures (shape-small-local-model / shape-mid-tier-model / shape-reasoning-model) that single-model runs skip, and exposes deltas like "qwen-7b fails analyzer-creative-media but gpt-4o-mini passes it" in a glance.

npm run matrix -- --models llama3.2:3b,qwen2.5-coder:7b-instruct-q4_K_M,qwen2.5:14b-instruct-q4_K_M

Outputs a dark-themed table — rows = fixtures, columns = models, cells = pass / fail / skip / errored with tooltips showing which checks failed.

Companion fix: evals/run.mjs gains a --json-out <path> flag that writes structured per-model results (matrix.mjs uses it; CI agents can use it too).

Numbers

• No tool surface change. Still 23 MCP tools.
• No platform count change. 60+ platforms (packs/platforms/*.yaml unchanged).
• 30 → 30 fixtures (no new fixtures; matrix is tooling, not coverage).
• Tarball grows ~10 KB for the knowledge pack. evals/matrix.mjs is NOT in the tarball — it's a maintainer/contributor tool, not a runtime artifact.

What's new in 1.6.1

Patch release. Adds Higgsfield as a target platform in both image and video categories. No code changes — pure YAML platform-pack additions and one eval fixture.

Higgsfield is a multi-model creative platform that exposes its own MCP server at https://mcp.higgsfield.ai/mcp. Inside one connection you get:

• Image: Soul 2.0, Soul Cinema, Soul Cast (character-consistent), Flux 2, Seedream 5, Nano Banana Pro, GPT Image 2
• Video: Cinema Studio, Sora 2, Veo 3.1, Kling 3.0, WAN 2.6, Seedance 2.0
• Workflows: Soul ID character training, Lipsync Studio, UGC Factory, Marketing Studio, virality_predictor

The 1.6.1 ClarifyPrompt platform entries surface Higgsfield's model identifiers and prompt-style conventions (long-form natural-language prose; composition + lighting + textures + mood; up to 4K images / 15 s video / Soul ID for character consistency) as syntax hints to the curator.

Recommended pattern: install both clarifyprompt-mcp AND Higgsfield's MCP in your client (Claude Desktop / Cursor / AI Butler / Claude Code). Use optimize_prompt(platform: 'higgsfield', ...) or compose_prompt(platform: 'higgsfield', ...) to compile, then pass the compiled prompt to Higgsfield's generate_image / generate_video tool. MCPs compose at the client; ClarifyPrompt stays at the "compile" layer.

29 → 30 eval fixtures. Same MCP tool surface as 1.6.0 (23 tools, 1 resource). No env-var changes.

What's new in 1.6.0

Four targeted additions across the engine's four pillars (memory / agentic / models / context), each shipped behind real eval fixtures. 3 new MCP tools (23 total). Fully back-compat with 1.5.x — no removed tools, no removed fields, no required env-var changes.

Memory — explicit fact CRUD (memory_remember, memory_forget, memory_list_facts)

Before 1.6, facts only entered persistent memory via reflection on save_outcome — implicit, LLM-extracted, after-the-fact. 1.6 adds the explicit path:

• memory_remember — directly insert a (subject, predicate, object) triple with explicit confidence. Source tagged user:explicit. Auto-embedded for future semantic retrieval.
• memory_forget — soft-delete (bi-temporal invalidated_at) a fact by id. Idempotent: re-forgetting an already-invalidated fact is a no-op and returns success: false cleanly.
• memory_list_facts — list live facts in a scope (default user), optionally filtered by predicate. Sorted by most-recently-observed.

This closes the obvious UX gap where the engine could only learn from outcomes — now users can say "remember I prefer X" directly.

Agentic — compose_prompt's new max_iterations revise loop

compose_prompt used to revise once (the critique's improvedPrompt replaced the optimization, if the verdict wasn't accept). 1.6 adds a loop:

{ "prompt": "...", "post_critique": true, "auto_revise": true, "max_iterations": 3 }

Each iteration after the first re-runs optimize + critique on the previous iteration's improved prompt. Stops at verdict=accept, no improvedPrompt to feed back, or the cap. pre_clarify only runs once (no point re-asking on a rewrite). The response includes a new iterations field showing how many fired. Hard cap of 5 to prevent cost runaways.

Models — per-stage model routing

Each compose stage can now target a different model:

{
  "prompt": "...",
  "clarify_model": "qwen2.5-coder:7b-instruct-q4_K_M",
  "optimize_model": "claude-sonnet-4-20250514",
  "critique_model": "gpt-4o-mini"
}

Run clarify on a cheap local model, optimize on the big-budget frontier model, critique on the cheap judge. The override flows through every layer — optimization.metadata.model and critique.judgeModel in the response reflect the actual model that ran each stage.

Context — git-state + environment signals

Two new signal collectors feed the Context Curator:

• bundle.git — current branch, short SHA, dirty flag, last 5 commit titles. Lets the engine ground prompts in "what you're iterating on" without you spelling it out. Detected via git rev-parse / git status / git log; fails soft when cwd isn't a repo.
• bundle.environment — nowIso / weekday / timezone (IANA from Intl.DateTimeFormat). Helps with time-sensitive prompts ("send this email tomorrow"). Pure JS, never fails.

Both are low-utility candidates in the curator (won't dominate budget) but surface as grounding sources when relevant.

Eval coverage

23 → 29 fixtures (6 new):

• 24 memory-remember-persists / 25 memory-forget-invalidates — Me1 CRUD round-trip
• 26 compose-loop-iterates — A1 loop infrastructure (new iterations_min / iterations_max checks)
• 27 compose-per-stage-models-honored — M1 per-stage routing (new optimization_model_eq / critique_model_eq checks)
• 28 context-includes-git-state / 29 context-includes-environment-time — C1 + C4 signals (new bundle_has_git / bundle_has_environment / git_branch_present checks)

Local baseline on qwen2.5-coder:7b: 25 passed / 1 failed / 3 skipped / 97% avg. The lone failure remains the persistent analyzer-creative-media model-class signal (untouched).

What's new in 1.5.2

The first release where CI's eval gate (against gpt-4o-mini) drove the diff. Three real fixes that the gate caught the moment we wired in the OPENAI_API_KEY secret:

• Memory store now supports any embedding dimension (#2). The persistent vec table was hardcoded to 768 dims (the nomic-embed-text default), so anyone configuring EMBED_MODEL=text-embedding-3-small (1536), voyage-3 (1024), embed-english-v3.0 (1024), or any non-768 model would hit Dimension mismatch: expected 768, got N on the first memory_search call. The store now derives the table name from the embedder's actual dimension and creates the dim-specific table at boot. Existing 768-dim installs are unaffected.
• LLM_TIMEOUT_MS env-var override on the LLM client. Default stays at 30s; users on slow hosted models can bump it. The eval workflow uses 120s for gpt-4o-mini.
• Eval harness hardened — no longer crashes when a tool throws an exception (the SDK returns plain-text error responses; the harness used to JSON.parse them and die). One bad fixture no longer tanks the whole run.
• Live evals badge. The evals.yml workflow runs on every push to main. The [![evals]](...) badge at the top of this README is its real-time status. Currently green at 20/0/3 · 100% on gpt-4o-mini.

No new MCP tools. No env-var surface changes (only an added optional LLM_TIMEOUT_MS). Fully back-compat with 1.5.x.

What's new in 1.5.1

A patch release on top of 1.5.0. Pure docs + ship-process improvements; runtime behavior is identical to 1.5.0.

• README marketing surfaces refreshed — the 1.5.0 release shipped with the README still on 1.4.0 in three places (headline blockquote, "What's new in X" heading, "cumulative through X" annotation). Every other version surface (package.json, package-lock.json, server.json, src/index.ts, CHANGELOG) was correct, but the prose drifted because nothing automated touched it. 1.5.1 fixes that.
• Two new ship-check audits — CP-11 (README marketing-surface coherence) hard-fails if any of the three above don't reference the current package.json#version. CP-12 (Platform-pack format validity) parses every packs/platforms/*.yaml and asserts schema validity. CP-11 was promoted to the user-scoped (cross-project) ship-check skill the same day, so future projects benefit too.
• No code changes. No new MCP tools. No new env vars. Same tarball anatomy as 1.5.0 plus a few hundred bytes of CHANGELOG.

What's new in 1.5.0

Built-in platforms become declarative. The 58+ hardcoded TypeScript platform arrays move to packs/platforms/*.yaml — adding a built-in platform is now a YAML edit, not a TS edit. The TypeScript layer becomes a runtime loader with a hardcoded fallback table. Malformed YAML can never soft-brick the server.

packs/platforms/
  chat.yaml       9 platforms
  code.yaml       9
  document.yaml   8
  image.yaml     10
  music.yaml      4
  video.yaml     11
  voice.yaml      7
  README.md      contributor docs

To add a new built-in platform: append an entry to the relevant category file, run npm run build, open a PR. No TS edit required. Custom-platform-via-runtime (register_platform) still works identically for user-installed platforms.

• Memory-layer eval coverage. The eval harness now supports setup: [{tool, args}, ...] — a list of MCP tool calls executed BEFORE the main input. Two new fixtures use it: one loads a knowledge pack inline and verifies the chunk surfaces in grounding.sources after the embed → store → retrieve → curate → ground pipeline; the other proves vector-search ranking quality. 23 fixtures total (was 20 in 1.4.0).
• Test infrastructure modernization. The integration + Day-2 test batteries used to assert literal version strings (1.3.0, 16 tools) and broke on every bump. Now they read EXPECTED_VERSION from package.json and assert presence of a tool set rather than a tool count. Future bumps don't break the tests.
• Adoption materials. docs/adoption/ ships with copy/paste-ready Show HN body, Reddit posts, Twitter thread, awesome-mcp-servers PR template, and catalog submission specs (mcp.so, Smithery, mcp-get, PulseMCP, modelcontextprotocol/servers).
• One new runtime dep: js-yaml promoted from devDependency for the platform loader (~200 KB).
• Same MCP tool surface as 1.4. 20 tools, 1 resource. No new tools; no removed tools; result shapes unchanged.

Previously in 1.4.0 — the composable pipeline

Four core operations as first-class MCP tools that compose. Use any tool standalone, or run the whole chain in one call:

  ┌─────────────┐     ┌─────────────────────┐     ┌──────────────┐
  │  clarify    │ →   │  ground OR optimize │ →   │   critique   │
  │  (optional) │     │       (core)        │     │  (optional)  │
  └─────────────┘     └─────────────────────┘     └──────────────┘

  one call = compose_prompt(prompt, [sources], post_critique, auto_revise, ...)

• clarify_with_user — Given an ambiguous draft, returns 1–3 targeted clarifying questions, each with a suggested_answer you can accept verbatim, optional 2–4 quick-pick options, and a dimension tag (audience/scope/format/length/tone/constraints/goal/platform). Short-circuits with clarificationNeeded: false on confident, well-formed prompts so it pipelines cleanly in front of optimize_prompt without a per-call latency tax.
• ground_prompt — The strict, retrieval-augmented variant of optimize_prompt. Caller-provided sources are pinned at the highest priority — above project rules, above pinned instructions — and tracked individually in the trace as user-source:N. Strict mode: zero non-empty sources → error, no silent fall-through. Per-source body cap (4000 chars) so a single huge paste can't dominate the budget.
• critique_prompt — LLM-as-judge. Scores a candidate prompt 0–10 across 5 default dimensions (clarity, specificity, intent_alignment, format_fitness, length_appropriateness) — or your own criteria — with per-dimension rationale + concrete suggestions, an overall score, and a verdict (accept / revise / reject). Below revise_threshold (default 7.0) it also returns an improvedPrompt you can drop in. Use it pre-flight ("is this prompt good enough for the expensive model?"), postmortem ("was the prompt the cause?"), or to A/B-pick the best of N optimization variants.
• compose_prompt — One MCP call runs the canonical pipeline. Auto-decides the ground vs. optimize branch from whether you passed sources. pre_clarify: 'auto' | 'always' | 'never'. post_critique: true adds a judge pass. auto_revise: true replaces final_prompt with the rewrite when the verdict isn't accept. Returns a per-stage stages audit array so the caller sees exactly what ran.
• Eval harness v0 — Deterministic regression tests under evals/. 20 YAML fixtures cover analyzer, shape, intent-overlay, grounding, clarify, critique, ground, and compose surfaces. npm run eval produces a console summary + self-contained dark-themed HTML report. Multi-model matrix is just bash: run LLM_MODEL=... npm run eval -- --report-path evals/report-X.html per model.
• CI-gated evals (opt-in) — When OPENAI_API_KEY is set as a repo secret, the eval harness runs in CI against gpt-4o-mini as a release gate. Off by default; nothing leaves your machine without the secret.
• 5 new MCP tools (20 total). optimize_prompt also gains a userProvidedSources injection point — both ground_prompt and compose_prompt use it under the hood, but it's available directly if you want explicit control without the strict-mode validation.

Carried over from 1.3: persistent memory + knowledge packs + reflective learning. The curator continues to score and fit grounding sources into the target model's remaining window. explain_last_curation still gives you a per-call breakdown of selected vs. rejected candidates with reasons.

What's in the box (cumulative through 1.12.0)

Documentation truncated — see the full README on GitHub.