docs-mcp

An MCP (Model Context Protocol) server that exposes Actian product documentation, GA4 content analytics, and Jenkins CI build status to Claude — enabling natural-language queries across all three sources in a single conversation.

Claude Desktop / Claude Code
        │
        │  JSON-RPC over stdio
        ▼
  docs-mcp
        │
        ├── search_docs      → Actian Markdown corpus (TF-IDF, swap for pgvector)
        ├── get_topic        → Full topic content by ID
        ├── list_topics      → Corpus index with product/type filters
        ├── get_content_gaps → GA4 zero-result search queries (mock → BigQuery)
        └── get_build_status → Jenkins publish pipeline status (mock → live API)

What this enables

Once connected, you can ask Claude:

"What are the top 10 search queries on the Actian docs site that returned no results this month?"

"Find every topic in the Analytics Engine 8.0 corpus that mentions the Tableau connector, and check whether the publish pipeline is currently green."

"Did the docs build pass? If it failed, search the corpus for the RPM upgrade procedure and show me the relevant steps."

---

Quick start

1. Clone and install

git clone https://github.com/Bipin-24/docs-mcp.git
cd docs-mcp
npm install
npm run build

2. Connect to Claude Desktop

Open your Claude Desktop config file:

OS	Path
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Windows	%APPDATA%\Claude\claude_desktop_config.json

Add this block (replace the path with your actual clone location):

{
  "mcpServers": {
    "actian-docs": {
      "command": "node",
      "args": ["/absolute/path/to/docs-mcp/dist/index.js"]
    }
  }
}

Restart Claude Desktop. You should see actian-docs in the tools list.

3. Connect to Claude Code

Drop a .mcp.json file in your project root:

{
  "mcpServers": {
    "actian-docs": {
      "command": "node",
      "args": ["../docs-mcp/dist/index.js"]
    }
  }
}

---

Tools

search_docs

Semantic search across the documentation corpus.

Input:
  query    string  required  Natural language search query
  product  string  optional  analytics-engine | ingres | actian-client | all
  version  string  optional  e.g. "8.0", "11.x"
  limit    number  optional  1–10, default 5

Output: ranked list of matching topics with excerpt and relevance score

Example prompt: "How do I upgrade Analytics Engine using RPM packages?"

---

get_topic

Retrieve the full Markdown content of a topic by ID.

Input:
  topic_id  string  required  Topic ID from search_docs results

Output: full topic content with metadata

---

list_topics

Browse the corpus index.

Input:
  product     string  optional  analytics-engine | ingres | actian-client | all
  topic_type  string  optional  concept | task | reference | troubleshooting | all

Output: topics grouped by product

---

get_content_gaps

Surfaces search queries that returned zero or very few results — i.e. documentation your users need but that doesn't exist yet.

Input:
  days          number  optional  Lookback window, default 30
  limit         number  optional  Max gaps to return, default 20
  min_searches  number  optional  Minimum search volume, default 2

Output: ranked gap list with gap type, search volume, and nearest existing topics

Example prompt: "What are the top missing documentation topics based on search data from the last 30 days?"

---

get_build_status

Returns the current Jenkins CI pipeline status for the docs publish job.

Input:
  job  string  optional  Jenkins job name, default "actian-docs-publish"

Output: latest build result, stage breakdown, and recent history

---

Architecture

Search

The server ships with a lightweight TF-IDF keyword search engine (src/lib/search.ts) that requires no external dependencies or API keys. It scores documents using term frequency with title and tag match bonuses.

To upgrade to embedding-based semantic search:

1. Add chromadb to package.json
2. Run npm run index to embed the corpus using the Python indexer (scripts/index_corpus.py)
3. Swap scoreTopics() in src/lib/search.ts for a Chroma similarity query

Live data

The server ships with realistic mock data for GA4 analytics and Jenkins. To switch to live sources, add credentials to .env:

cp .env.example .env
# Edit .env with your BigQuery project ID and Jenkins token

See .env.example for all available configuration options.

The BigQuery query for GA4 Site Search export is in scripts/ga4_export.sql.

---

Project structure

docs-mcp/
├── src/
│   ├── index.ts              # MCP server — tool registry and routing
│   ├── tools/
│   │   ├── searchDocs.ts     # search_docs handler
│   │   ├── getTopic.ts       # get_topic handler
│   │   ├── listTopics.ts     # list_topics handler
│   │   ├── getContentGaps.ts # get_content_gaps handler
│   │   └── getBuildStatus.ts # get_build_status handler
│   ├── data/
│   │   ├── corpus.ts         # Sample Actian documentation topics
│   │   ├── analyticsData.ts  # Mock GA4 site search data
│   │   └── jenkinsData.ts    # Mock Jenkins build history
│   └── lib/
│       └── search.ts         # TF-IDF search engine
├── scripts/
│   └── ga4_export.sql        # BigQuery query for real GA4 export
├── config/
│   ├── claude_desktop_config.json  # Claude Desktop setup
│   └── mcp.json                    # Claude Code project setup
├── .env.example
└── tsconfig.json

---

Tech stack

• Runtime: Node.js 18+ / TypeScript
• MCP SDK: @modelcontextprotocol/sdk
• Search: TF-IDF (built-in) → upgradeable to pgvector / Chroma
• Analytics: Mock GA4 data → upgradeable to BigQuery
• CI status: Mock Jenkins data → upgradeable to live Jenkins REST API

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Related projects

• doc-rag-chatbot — RAG pipeline and chat UI over a documentation corpus
• doc-content-gap-finder — CLI tool that finds undocumented topics from search query data
• IA Playbook — Reference architecture for AI-readable documentation

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Author

Bipin Pandey — Principal Information Architect
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