docchat-mcp 0.1.0

Turn your API docs into an intelligent MCP server with deterministic routing and layered knowledge injection.

DocChat MCP

Turn your API docs into an intelligent MCP server with deterministic routing and layered knowledge injection.

DocChat transforms your API documentation into a smart MCP server. AI assistants like Claude Code can query your API docs through natural language — with ~80% of queries resolved via deterministic routing (zero LLM cost).

How It Works

User question → Deterministic routing (trigger keywords / field names / feed codes)
                   ↓ matched                    ↓ not matched
           Load knowledge files          Return "needs LLM fallback"
                   ↓
    AI client receives structured docs and generates answer

The engine handles routing + knowledge retrieval. The AI client (Claude Code, Claude Desktop, etc.) handles reasoning.

Features

• Deterministic routing — trigger keywords, field names, and feed codes match queries without LLM
• Layered knowledge injection — feed-level, overview, shared, and topic-matched knowledge
• Custom dimensions — organize feeds by any hierarchy (product, version, region, etc.)
• MCP native — 4 tools, 3 resources, 2 prompts — works with any MCP client
• Zero LLM dependency — the engine only provides data; AI reasoning is done by the client
• CLI tooling — init / build / validate / serve / mcp

Quick Start

# Install
pip install docchat-mcp

# Initialize a knowledge pack
docchat init --name my-api

# Write your docs (see docs/writing-guide.md or use docchat-author skill)
# Each feed gets: META.yaml (triggers + fields) + GUIDE.md (usage guide)

# Validate format
docchat validate

# Build index
docchat build

# Start MCP server
docchat mcp        # stdio mode (for Claude Code)
docchat serve      # HTTP mode (for remote access)

Connect to Claude Code

# Add as MCP server (stdio)
claude mcp add my-api -- docchat mcp --dir ./my-api-docs/

# Or via uvx (zero install)
claude mcp add my-api -- uvx docchat-mcp mcp --dir ./my-api-docs/

Knowledge Pack Structure

my-api/
├── docchat.yaml          # Pack config (name, dimensions, assistant)
├── _shared/              # Shared knowledge (error codes, auth, etc.)
│   ├── INDEX.yaml        # Topic keywords for matching
│   └── error_codes.md
├── _overview/            # API overview
│   └── INDEX.md
└── feeds/                # One directory per API endpoint
    ├── get-users/
    │   ├── META.yaml     # Triggers, fields, description
    │   ├── GUIDE.md      # Usage guide
    │   ├── FAQ.md        # Optional: troubleshooting
    │   └── fields/       # Optional: field reference
    └── get-posts/
        ├── META.yaml
        └── GUIDE.md

See docs/knowledge-pack-format.md for the full specification.

Custom Dimensions

Organize feeds by any hierarchy via docchat.yaml:

# Simple API (all feeds flat)
dimensions: []

# Two dimensions (product x sport)
dimensions:
  - key: product
    values: { rest: "REST API", ws: "WebSocket" }
  - key: sport
    values: { soccer: "Soccer", basketball: "Basketball" }

MCP Tools

Tool	Description
list_feeds	List all available feeds
search_by_field	Find feeds by field name
get_feed_info	Get feed details + documentation
route_question	Route a query and return matched feeds + knowledge

Why Not RAG?

Approach	Limitation	DocChat
Swagger UI	Browse only, no Q&A	Natural language queries
ChatGPT + docs	Full context dump, token overflow	Layered injection
RAG (embeddings)	Embedding quality varies, routing opaque	Deterministic routing (~80% zero LLM)
No MCP	AI can't access docs	MCP native, Claude Code direct

Demo

See it in action at docchat.site

License

MIT