knowflow

Documentation has always been a knowledge layer. knowflow makes it one that AI can actually reach.

knowflow is an MCP (Model Context Protocol) server built by an information architect who got tired of context-switching between four tools to answer one question: what are users not finding?

It connects a documentation corpus, GA4 search analytics, a Jenkins CI pipeline, and a RAGAS-style evaluation layer to Claude, in a single conversation.

Runs with no credentials. The corpus, GA4 analytics, Jenkins data, and evaluation all ship as working local implementations, so you can clone, build, and query it in about two minutes. Every one has a documented upgrade path to live data. See Upgrading to live data.

Claude Desktop / Claude Code
        │
        │  JSON-RPC over stdio
        ▼
   knowflow
        │
        ├── search_docs          → TF-IDF corpus search (upgradeable to pgvector / ChromaDB)
        ├── get_topic            → Full topic content by ID
        ├── list_topics          → Corpus index with product and type filters
        ├── get_content_gaps     → GA4 zero-result queries → ranked content gap list
        ├── get_build_status     → Jenkins publish pipeline status
        └── evaluate_pipeline    → RAGAS-style eval: relevance · faithfulness · recall

Demo

Related MCP server: Synaptex

Why this exists

Every documentation team asks the same question: what should we write next?

The answer used to live in three or four separate places: search analytics in GA4, existing content in a docs site, topic hierarchy in a spreadsheet, build status in Jenkins. Getting from "what are users not finding?" to "here is a drafted topic" took hours of context-switching.

knowflow collapses that into a single conversation.

get_content_gaps          "47 users searched for X and got nothing"
       ↓
search_docs               "nearest existing topic: rpm-upgrade-8.0"
       ↓
get_topic                 "here is the full content as context"
       ↓
Claude drafts             the missing section in under two minutes
       ↓
evaluate_pipeline         "faithfulness: 0.91 · relevance: 0.87 · recall: 0.74"
       ↓
IA reviews → publishes → gap closes → loop repeats

The part that changed most is not the speed. It is the signal. You know what to write before a support ticket tells you.

What you can ask Claude once connected

"What are the top ten search queries from the last 30 days that returned no results?"

"Run evaluate_pipeline with report_format markdown. Show me which queries are underperforming and why."

"Find every topic in the corpus that mentions the Tableau connector. Did the docs build pass today?"

"A user searched for 'silent RPM install' 47 times and got nothing. Find the nearest existing topic and draft the missing section."

"Which topics have not been reviewed in over 90 days? Flag them as potential faithfulness risks."

Quick start

1. Clone and install

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

2. Connect to Claude Desktop

Open your Claude Desktop config:

Add this block:

{
  "mcpServers": {
    "knowflow": {
      "command": "node",
      "args": ["/absolute/path/to/knowflow/dist/index.js"]
    }
  }
}

Restart Claude Desktop. You should see knowflow in the tools list.

3. Connect to Claude Code

Drop a .mcp.json in your project root:

{
  "mcpServers": {
    "knowflow": {
      "command": "node",
      "args": ["../knowflow/dist/index.js"]
    }
  }
}

Tools

search_docs

Keyword search across the documentation corpus, ranked by TF-IDF. Upgradeable to embedding-based semantic search. See Architecture.

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

get_topic

Retrieve full Markdown content of a topic by ID.

topic_id  string  required  Topic ID from search_docs results

list_topics

Browse the corpus index with optional filters.

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

get_content_gaps

Surface search queries that returned zero or few results, meaning content your users need but that does not exist yet.

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

Returns each gap with gap_type (missing_content or low_discoverability), nearest existing topic, search volume, and recommended action.

get_build_status

Check Jenkins CI/CD publish pipeline status.

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

evaluate_pipeline

Run a RAGAS-style evaluation across the pipeline.

queries        string[]  optional  Test queries. Uses a default set of 10 if omitted.
report_format  string    optional  summary | detailed | markdown  (default: summary)

Scores are computed by deterministic heuristics rather than an LLM judge, so the tool runs offline and returns the same result every time. That makes it useful as a regression check on retrieval changes. For production scoring, swap in the RAGAS Python library. See Evaluation.

Architecture

Search

Ships with a lightweight TF-IDF engine, with no external dependencies or API keys. To upgrade to embedding-based semantic search:

1. Add chromadb or pgvector to package.json

2. Run scripts/index_corpus.py to embed the corpus

3. Swap scoreTopics() in src/lib/search.ts for a vector similarity query

Evaluation

src/lib/evaluator.ts uses deterministic heuristics as a RAGAS approximation, so no LLM API calls are required to run it. Replace it with the RAGAS Python library for production use with an LLM judge.

Live data

Ships with realistic mock data for GA4 and Jenkins. To connect live sources:

cp .env.example .env
# Fill in BIGQUERY_PROJECT_ID, JENKINS_URL, JENKINS_TOKEN

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

Project structure

knowflow/
├── src/
│   ├── index.ts                  # MCP server — tool registry and router
│   ├── tools/
│   │   ├── searchDocs.ts
│   │   ├── getTopic.ts
│   │   ├── listTopics.ts
│   │   ├── getContentGaps.ts
│   │   ├── getBuildStatus.ts
│   │   └── evaluatePipeline.ts   # RAGAS-style evaluation
│   ├── data/
│   │   └── corpus.ts             # Sample documentation topics
│   └── lib/
│       ├── search.ts             # TF-IDF search engine
│       └── evaluator.ts          # Evaluation engine
├── scripts/
│   └── ga4_export.sql            # BigQuery query for live GA4 export
├── .env.example
├── package.json
└── tsconfig.json

Upgrading to live data

Tech stack

• Runtime: Node.js 18+ / TypeScript

• Protocol: @modelcontextprotocol/sdk

• Search: TF-IDF, upgradeable to pgvector / ChromaDB

• Evaluation: Deterministic RAGAS approximation, upgradeable to RAGAS Python

• Analytics: Mock GA4, upgradeable to BigQuery

• CI: Mock Jenkins, upgradeable to the Jenkins REST API

Related projects

• docs-mcp — the base retrieval layer this evolved from: five MCP tools over a docs corpus, analytics, and CI

• Documentation-AI-Assistant — RAG pipeline and chat UI over a documentation corpus

• knowledge-graphs-for-ia — typed knowledge graph builder with relationship-based retrieval

• IA Playbook — AI content governance framework for RAG-ready documentation

License

MIT. See LICENSE.

Author

Bipin Pandey — Principal Information Architect
Building the knowledge layer that humans and AI systems both depend on.

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