How do I install Bighub?

Bighub is a local plugin. Install it using npm package: @bighub/bighub-mcp and add the generated configuration snippet to your AI app's MCP config file. Then restart your AI app.

What credentials does Bighub need?

Bighub requires the following credentials or environment variables: BIGHUB_API_KEY, BIGHUB_BEARER_TOKEN, BIGHUB_BASE_URL. You can find setup instructions on the server detail page.

What AI apps work with Bighub?

Bighub uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Bighub MCP Server

by User

Developer ToolsLow Risk10.0Local

Free

Decision learning for AI agent actions. Evaluate, score, decide, and learn from outcomes.

About

Decision learning for AI agent actions. Evaluate, score, decide, and learn from outcomes.

Security Report

10.0

Low Risk10.0Low Risk

Valid MCP server (2 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.

7 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

file_system

Check that this permission is expected for this type of plugin.

HTTP Network Access

Connects to external APIs or services over the internet.

What You'll Need

Set these up before or after installing:

BIGHUB API key for authenticating requests (X-API-Key header).Required

Environment variable: BIGHUB_API_KEY

Alternative authentication via Bearer token. Use this or BIGHUB_API_KEY.Required

Environment variable: BIGHUB_BEARER_TOKEN

BIGHUB API base URL.Optional

Environment variable: BIGHUB_BASE_URL

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-bighub-mcp": {
      "env": {
        "BIGHUB_API_KEY": "your-bighub-api-key-here",
        "BIGHUB_BASE_URL": "your-bighub-base-url-here",
        "BIGHUB_BEARER_TOKEN": "your-bighub-bearer-token-here"
      },
      "args": [
        "-y",
        "@bighub/bighub-mcp"
      ],
      "command": "npx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

BIGHUB

Decision learning for AI agents.

BIGHUB is a decision layer that evaluates agent actions, returns structured recommendations, and learns from real outcomes over time.

Agent proposes action
        ↓
BIGHUB evaluates in context
        ↓
returns recommendation + confidence + rationale
        ↓
agent / runtime decides what to do
        ↓
real outcome is reported
        ↓
future recommendations improve over time

Packages

Package	Language	Install	Description
bighub	Python	`pip install bighub`	Core SDK — evaluate actions, receive recommendations, report outcomes, retrieve learned signals.
bighub-openai	Python	`pip install bighub-openai`	OpenAI Responses API adapter — evaluate tool calls, surface recommendations, learn from outcomes.
@bighub/bighub-mcp	TypeScript	`npm install @bighub/bighub-mcp`	MCP server — evaluate agent actions from any MCP client.
bighub-anthropic	Python	—	Anthropic adapter — coming soon.
bighub-openai (JS)	TypeScript	—	OpenAI adapter for Node.js — coming soon.

How it works

Static guardrails	BIGHUB
Fixed allow / block logic	Structured recommendation in context
Rules only	Constraints + precedents + calibration + outcomes
One-off judgment	Trajectory- and history-aware evaluation
Prediction only	Prediction compared with real outcomes
Static thresholds	Recommendation quality improves over time

Quickstart (Python SDK)

pip install bighub

import os
from bighub import BighubClient

client = BighubClient(api_key=os.getenv("BIGHUB_API_KEY"))

# 1. Submit an action for evaluation
result = client.actions.submit(
    action="update_price",
    value=150.0,
    domain="pricing",
    actor="AI_AGENT_001",
)

# 2. Inspect the recommendation
print(result["recommendation"])             # proceed, proceed_with_caution, review_recommended, do_not_proceed
print(result["recommendation_confidence"])   # high, medium, low
print(result["risk_score"])                  # 0.0 – 1.0

# 3. Act based on the recommendation
if result["recommendation"] in ("proceed", "proceed_with_caution"):
    execute_action()

    # 4. Report the real outcome
    client.outcomes.report(
        request_id=result["request_id"],
        status="SUCCESS",
        description="Price updated, no negative impact observed",
    )
elif result["recommendation"] == "review_recommended":
    request_human_review()

client.close()

Core loop: evaluate → recommend → act → report outcome → learn.

Structured recommendation

BIGHUB primarily returns:

recommendation — what to do (proceed, proceed_with_caution, review_recommended, do_not_proceed)
recommendation_confidence — how confident (high, medium, low)
risk_score — aggregated risk (0–1)
enforcement_mode — how the recommendation is applied (advisory, review, enforced)
decision_intelligence — rationale, evidence status, trajectory health, alternatives

Legacy fields such as allowed, result, and reason may still appear for backward compatibility, but they are not the primary product surface.

Trajectory-aware evaluation

BIGHUB evaluates actions not only in isolation, but also in the context of what happened before. As outcomes accumulate, similar sequences and prior decisions improve future recommendations.

For costly and multi-step workflows, trajectory-aware signals mean the same action may be judged differently depending on what happened earlier in the sequence.

Free BETA

Current Free BETA limits:

3 agents
2,500 actions / month
30 days history
1 environment

Repository layout

├── sdk/
│   └── python/
├── adapters/
│   ├── python/
│   │   ├── openai/
│   │   └── anthropic/
│   └── js/
│       └── openai/
├── servers/
│   └── mcp/
└── examples/