M · MCP Server

MCP Server

Connect any MCP-aware AI client (Claude Desktop, Cursor, others) to the framework. Tools to list, search, and read pages without trusting a hosted chat.

The framework exposes a Model Context Protocol server at https://decision-grade.ai/api/mcp. Any MCP-aware AI client can connect, list available tools, and read the framework directly from the user's chosen AI.

This is the on-doctrine surface for AI access to the framework. The user controls which AI runs the queries. The framework just publishes data with a typed interface.

Note

Three minute read. Endpoint: https://decision-grade.ai/api/mcp. Transport: HTTP + JSON-RPC 2.0. Four tools: list_pages, get_page, search, get_full_framework. Two readable resources: llms.txt, llms-full.txt. No authentication, no rate limit beyond Cloudflare's defaults.

Why an MCP server

The framework's Zero Trust posture says the customer should not have to trust the verifier. A hosted chat agent inserts the framework's hosting choices into the trust path. An MCP server keeps the user's AI of choice in that path. The framework is just published data.

User controls the AI

Connect from Claude Desktop, Cursor, Windsurf, or any MCP-aware client. The AI you trust does the reading and reasoning.

Framework controls only the data

The server returns markdown content and search results. It does not synthesize answers, refuse questions, or modify what the AI sees.

Tools

list_pages

Returns all framework pages with id, title, num, and description. Use this first to discover what is available.

Arguments: none.

Returns: JSON array of page summaries.

get_page

Fetch the full markdown content of one page.

Arguments: slug (string). One of introduction, the-frame, the-doctrine, buyers-checklist, lane-discipline, watchlist, about.

Returns: the full page markdown including callouts, tables, and mermaid diagrams.

Text search across the entire framework. Returns matching sections grouped by page, with snippets.

Arguments: query (string), optional limit (number, default 8, max 20).

Returns: JSON with total count and results array of section matches.

get_full_framework

Return the entire framework as a single document (the llms-full.txt bundle).

Arguments: none.

Returns: ~70 KB of plain text. Use when you want the whole framework in one fetch.

Connect from Claude Desktop

Edit your Claude Desktop config (Settings → Developer → Edit Config), then add:

{
  "mcpServers": {
    "decision-grade": {
      "url": "https://decision-grade.ai/api/mcp"
    }
  }
}

Restart Claude Desktop. The decision-grade tools appear in the tools menu. Ask Claude anything about AI verification and it will fetch from the framework.

Connect from Cursor

In Cursor settings, add an MCP server entry:

Name: decision-grade
Type: HTTP
URL: https://decision-grade.ai/api/mcp

The four tools become available in your Cursor agent.

Connect from any other client

The transport is plain HTTP + JSON-RPC 2.0. Any client that speaks MCP over HTTP can connect.

# List available tools
curl -X POST https://decision-grade.ai/api/mcp \
  -H "content-type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

# Search the framework
curl -X POST https://decision-grade.ai/api/mcp \
  -H "content-type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"search","arguments":{"query":"verification deficit","limit":5}}}'

What this is not

Not a chat agent

The server does not generate text or answer questions. It returns data. The AI you connect does the reasoning.

Not authenticated

Public read-only endpoint. No accounts, no API keys. The framework is published openly.

Not stateful

Each request stands alone. There is no session, no memory, no personalization.

Verifying the server

The same Zero Trust posture applies to this server. You can verify it operates as documented:

Source is public. The Cloudflare Pages Function code is at github.com/DavidVALIS/decision-grade/blob/main/functions/api/mcp.ts.
Output is deterministic. Same query yields the same result.
Content is canonical. All tool responses derive from llms-full.txt and search-index.json, which are also served at the site root.
No hidden behavior. The server does not call external LLMs or transform content; it only reads from the published bundle.

If you observe a discrepancy between what the MCP server returns and what is published on the site, open an issue.