Check-In Doc MCP Server: A Handy Way to Search Only the Docs You Trust

Labels: , , , , , , ,

Ever wished you could ask a question and have the answer come only from a handful of trusted documentation sites—no random blogs, no stale forum posts? That’s exactly what the Check-In Doc MCP Server does. It’s a lightweight Model Context Protocol (MCP) server you can run locally (or host) to funnel questions to selected documentation domains and get a clean AI-generated answer back.

What It Is

The project (GitHub: https://github.com/fboucher/check-in-doc-mcp) is a Dockerized MCP server that:

  • Accepts a user question.
  • Calls the Reka AI Research API with constraints (only allowed domains).
  • Returns a synthesized answer based on live documentation retrieval.

You control which sites are searchable by passing a comma‑separated list of domains (e.g. docs.reka.ai,docs.github.com). That keeps > results focused, reliable, and relevant.

What Is the Reka AI Research API?

Reka AI’s Research API lets you blend language model reasoning with targeted, on‑the‑fly web/document retrieval. Instead of a model hallucinating an answer from static training data, it can:

  • Perform limited domain‑scoped web searches.
  • Pull fresh snippets.
  • Integrate them into a structured response.

In this project, we use the research feature with a web_search block specifying:

  • allowed_domains: Only the documentation sites you trust.
  • max_uses: Caps how many retrieval calls it makes per query (controls cost & latency).

Details used here:

  • Model: reka-flash-research
  • Endpoint: http://api.reka.ai/v1/chat/completions
  • Auth: Bearer API key (generated from the Reka dashboard: https://link.reka.ai/free)

How It Works Internally

The core logic lives in ResearchService (src/Domain/ResearchService.cs). Simplified flow:

  1. Initialization
    Stores the API key + array of allowed domains, sets model & endpoint, logs a safe startup message.

  2. Build Request Payload
    The CheckInDoc(string question) method creates a JSON payload:

    var requestPayload = new {
  model,
  messages = new[] { new { role = "user", content = question } },
  research = new {
    web_search = new {
      allowed_domains = allowedDomains,
      max_uses = 4
    }
  }
};

  3. Send Request
    Creates a HttpRequestMessage (POST), adds Authorization: Bearer <APIKEY>, sends JSON to Reka.

  4. Parse Response
    Deserializes into a RekaResponse domain object, returns the first answer string.

Adding It to VS Code (MCP Extension)

You can run it as a Docker-based MCP server. Two simple approaches:

Option 1: Via “Add MCP Server” UI

  1. In VS Code (with MCP extension), click Add MCP Server.
  2. Choose type: Docker image.
  3. Image name: fboucher/check-in-doc-mcp.
  4. Enter allowed domains and your Reka API key when prompted.

Option 2: Via mcp.json (Recommended)

Alternatively, you can manually configure it in your mcp.json file. This will make sure your API key isn't displayed in plain text. Add or merge this configuration:

{
  "servers": {
    "check-in-docs": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "ALLOWED_DOMAINS=${input:allowed_domains}
        ",
        "-e",
        "APIKEY=${input:apikey}",
        "fboucher/check-in-doc-mcp"
      ]
    }
  },
  "inputs": [
    {
      "id": "allowed_domains",
      "type": "promptString",
      "description": "Enter the comma-separated list of documentation domains to allow (e.g. docs.reka.ai,docs.github.com):"
    },
    {
      "id": "apikey",
      "type": "promptString",
      "password": true,
      "description": "Enter your Reka Platform API key:"
    }
  ]
}

How to Use It

To use it ask to Check In Doc something or You can now use the SearchInDoc tool in your MCP-enabled environment. Just ask a question, and it will search only the specified documentation domains.

Final Thoughts

It’s intentionally simple—no giant orchestration layer. Just a clean bridge between a question, curated domains, and a research-enabled model. Sometimes that’s all you need to get focused, trustworthy answers.

If this sparks an idea, clone it and adapt away. If you improve it (citations, richer error handling, multi-turn context)—send a PR!

Watch a quick demo


Links & References