Yes, Wafergraph is free to use.

How do I install Wafergraph?

Wafergraph is a remote plugin. You do not need to install anything locally. Add the server URL to your AI app's MCP configuration and you are ready to go.

Is Wafergraph safe to use?

Yes. Wafergraph passed MCP Marketplace's automated security scan with a score of 10/10 (low risk). Every server on MCP Marketplace is security-scanned before it's listed; see the full security report on this page for the findings and permissions.

What AI apps work with Wafergraph?

Wafergraph 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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Wafergraph MCP Server

by Jasonpalmer1

Developer ToolsLow Risk10.0MCP RegistryRemote

Free

Server data from the Official MCP Registry

Read-only MCP server for wafergraph.com's semiconductor & AI supply-chain data: 30 tools, no auth.

About

Read-only MCP server for wafergraph.com's semiconductor & AI supply-chain data: 30 tools, no auth.

Remote endpoints: streamable-http: https://wafergraph-mcp.jwpalm99.workers.dev/mcp

Security Report

10.0

Low Risk10.0Low Risk

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

Endpoint verified · Open access · No issues 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.

database

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

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.

How to Connect

Remote Plugin

No local installation needed. Your AI client connects to the remote endpoint directly.

Add this to your MCP configuration to connect:

{
  "mcpServers": {
    "com-wafergraph-wafergraph-mcp": {
      "url": "https://wafergraph-mcp.jwpalm99.workers.dev/mcp"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

wafergraph-mcp

A remote MCP server exposing wafergraph.com's semiconductor & AI supply-chain dataset — 565 companies across 12 segments, the supplier/customer graph, and a 74-deal M&A corpus — as 30 read-only tools any MCP-speaking AI agent can call directly.

No auth, no cost, read-only. Streamable HTTP transport at /mcp. Human landing page at /.

Live: https://mcp.wafergraph.com

Tools

Tool	Purpose
`search_companies({query?, segment?, country?})`	Name/description search with segment & country filters. Compact list, capped at 25, with a total match count.
`get_company({id})`	Full allowed profile for one company (by id or name) plus its supplier/customer edges.
`get_segments()`	The 12-segment taxonomy (+ subsegments, market_position enum) with live company counts.
`get_supply_chain({id, direction, depth})`	Walk the supplier/customer graph from a focal company up to 2 tiers up (`"up"`), down (`"down"`), or `"both"`.
`get_deals({query?, segment?})`	Search the M&A deal corpus. Compact list, capped at 30, with a total match count.
`compare_companies({ids})`	Side-by-side comparison of 2-6 companies on aligned fields, plus their shared and unique supply-chain counterparties.
`get_country_exposure({segment?})`	Geographic concentration: which countries host a segment's companies, ranked by count, with disclosed market-cap coverage.
`find_chokepoints({segment?, limit?})`	Rank chokepoints by downstream dependency weighted by market position. Transparent heuristic, not a proprietary risk model.
`analyze_portfolio_exposure({holdings})`	Map tickers or ids to segment/country exposure and flag suppliers that several holdings share. Not investment advice.

Screening & discovery

Tool	Purpose
`filter_companies({...})`	Structured multi-criteria screen (segment, subsegment, country, position, public, market-cap band, has_ticker) with sorting and pagination.
`list_subsegments({segment?})`	Every subsegment with its live company count and parent segment.
`get_subsegment({segment, subsegment})`	All companies in one subsegment with position and country breakdowns.
`find_similar_companies({id})`	Nearest structural neighbours by Jaccard similarity over segment tags and shared supply-chain counterparties.
`rank_by_market_cap({segment?, country?, limit?})`	Largest companies by disclosed market cap, always with its coverage ratio.
`resolve_ticker({inputs})`	Batch-resolve up to 25 tickers, names, or ids to canonical companies, with suggestions on a miss.

Geography & structure

Tool	Purpose
`list_countries({segment?})`	Every country with company counts, segment mix, and public/private split.
`get_country_profile({country})`	One country in depth, including how many supply relationships cross its border in each direction.
`compare_countries({countries})`	2-5 countries side by side, with uniquely-present and dominant segments.
`get_segment_leaders({segment?})`	Who occupies the monopoly and leader positions in a segment.
`get_upstream_concentration({id})`	One company's supplier mix by country and segment with an HHI index and edge-coverage context.

Graph analysis

Tool	Purpose
`find_paths_between({from, to, max_depth?})`	Documented supply paths between two companies, shortest first.
`simulate_disruption({company_id?	country?
`find_single_source_dependencies({segment?, country?})`	Customer/subsegment pairs served by exactly one documented supplier.
`rank_by_connectivity({metric?, limit?})`	Rank by documented degree. Measures documentation density, not real-world criticality.
`find_common_suppliers({ids?	segment?})`

Deals & dataset

Tool	Purpose
`get_deal({id})`	One M&A deal in full, with parties resolved to companies where possible.
`find_deals_by_company({id})`	Every deal a company took part in, split by role, with the match method exposed.
`get_ma_activity_summary()`	Deal counts and disclosed values by year, type, and status.
`find_consolidation_hotspots()`	Which segments are consolidating, with unresolvable deals counted rather than dropped.
`get_dataset_stats()`	What this dataset contains, how fresh it is, and where it is thin. Call it to learn what the data cannot answer.

Every response includes:

data — the payload.
attribution — compiled-by/sources block (SEC, Wikidata, Wikipedia, GLEIF) plus a company_url back to the full sourced profile on wafergraph.com.
links — { report, newsletter } pointing back to wafergraph.com and its hand-scored Vendor Exposure Review.

Field discipline

Company records include key_products as of 2026-07-19. That field in wafergraph's own dataset was originally bulk-drafted with a known fabrication/mis-scope history and was withheld here until a verified deep-fill landed upstream; it now has (confirmed live) 564/565 companies filled, with only sk_enpulse empty (defunct, absorbed into SKC 2025-12-23). Every field shipped here — name, ticker, market_cap_usd_b, segments, one_liner, market_position, key_products, key_customers/key_suppliers graph edges, deals — is established, trust-checked data. Fields are still whitelisted (not blacklisted) in src/types.ts (toAllowedCompany), so anything added upstream later stays excluded by default until deliberately added.

What this server records

Counters only, never identities. No IPs, no query contents or tool arguments, no user identifiers, no cookies, no PII. Specifically: how many times each tool was called per day, how many distinct sessions opened per day, and which client software connected (the clientInfo name and version your MCP client already sends in the initialize handshake, e.g. claude-code@2.1.0).

Sessions are not users — one person reconnecting counts several times. Nothing recorded here can identify who you are.

Install

Claude Code

claude mcp add --transport http wafergraph https://mcp.wafergraph.com/mcp

claude.ai (custom connector)

Settings → Connectors → Add custom connector → paste:

https://mcp.wafergraph.com/mcp

Generic MCP client (Streamable HTTP)

{
  "mcpServers": {
    "wafergraph": {
      "url": "https://mcp.wafergraph.com/mcp"
    }
  }
}

Architecture

Cloudflare Worker, TypeScript, built on the agents package's McpAgent (Durable-Object-backed Streamable HTTP MCP server — free on the Workers Free plan; SQLite storage backend). See CLAUDE.md for the file map and the data-source-mode decision (hybrid live-fetch + one vendored snapshot, discovered by E2E-testing the upstream endpoints rather than assumed).

Development

npm install
npm run dev       # wrangler dev, local
npm run typecheck # tsc --noEmit
npm run deploy    # wrangler deploy

Attribution & licensing

See ATTRIBUTION.md for the full source/license breakdown (SEC EDGAR public domain, Wikidata CC0, Wikipedia CC BY-SA 4.0 — attribution required, GLEIF). Short version: free to use, credit wafergraph.com, not financial advice.

This is an independent project built against wafergraph.com's public dataset; it is not an official wafergraph product.

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