context-index-mcp

A lightweight context index MCP server for AI agents. Maps keywords to file paths instantly. No embeddings, no vector DB, no API keys — just a JSON file and a scoring function.

Author: Marcus Low Wern Chien (marcuslowwernchien@gmail.com)

How It Works

• Storage: Plain index.json file (array of entries).

• Transport: stdio (MCP standard) — spawned on demand.

• Search: Keyword scoring against tags, title, and description.

• Speed: Sub-100ms per lookup.

Related MCP server: LocalNest MCP

Tools

• lookup: Search by keyword. Returns top 5 matches with file paths and read instructions.

• add: Upsert entry by file. Omitted fields are preserved (pass "note": "" to clear). Warns if file doesn't exist.

• list: List all entries.

• remove: Remove entry by file.

• doctor: Checks health. Reports missing files on disk and unindexed context/**/*.md files.

Installation

git clone https://github.com/butler-kasagi/context-index-mcp.git
cd context-index-mcp
npm install

Note: index.json is gitignored — your data won't be overwritten by a git pull. The server creates it on the first add.

Configuration

Single Agent (mcporter)

Add to config/mcporter.json:

{
  "mcpServers": {
    "context-index": {
      "command": "node",
      "args": ["/path/to/context-index-mcp/index.js"],
      "env": {
        "CONTEXT_INDEX_WORKSPACE": "/path/to/your/workspace"
      }
    }
  }
}

Note: If CONTEXT_INDEX_WORKSPACE is unset, the server attempts to infer the workspace from existing index paths or the nearest context/ directory before falling back to process.cwd().

Multi-Agent Setup (OpenClaw)

Each agent has its own workspace and config/mcporter.json.

Method 1: Isolated Copy (Recommended for Secondary Agents)

Copy the server into the secondary agent's workspace. This ensures __dirname resolves locally, keeping the agent's index data safely isolated.

Step 1: Copy the server

mkdir -p /absolute/path/to/workspace-agent/mcp-servers/context-index
cp /absolute/path/to/primary-workspace/mcp-servers/context-index/index.js \
   /absolute/path/to/workspace-agent/mcp-servers/context-index/index.js

Step 2: Install dependencies

cd /absolute/path/to/workspace-agent/mcp-servers/context-index
npm init -y
npm install @modelcontextprotocol/sdk

Step 3: Create config/mcporter.json

{
  "mcpServers": {
    "context-index": {
      "command": "node",
      "args": ["/absolute/path/to/workspace-agent/mcp-servers/context-index/index.js"],
      "env": {
        "CONTEXT_INDEX_WORKSPACE": "/absolute/path/to/workspace-agent"
      }
    }
  }
}

⚠️ Crucial rules for secondary agents:

• Use absolute paths everywhere in the JSON config.

• If mcporter is not installed, install it globally: npm install -g mcporter.

Step 4: Verify the setup Run from the secondary workspace root:

cd /absolute/path/to/workspace-agent
mcporter call context-index doctor

Expected healthy output:

Index health (0 entries, workspace: /absolute/path/to/workspace-agent [CONTEXT_INDEX_WORKSPACE env var])
✅ All entries point to existing files, and every context/*.md file is indexed.

Method 2: Shared Binary (Advanced)

Share one index.js across agents. Warning: You must set CONTEXT_INDEX_PATH in every secondary agent's config. If omitted, the __dirname fallback will overwrite the primary agent's index.json!

Secondary Agent config/mcporter.json:

{
  "mcpServers": {
    "context-index": {
      "command": "node",
      "args": ["/absolute/path/to/primary/mcp-servers/context-index/index.js"],
      "env": {
        "CONTEXT_INDEX_WORKSPACE": "/absolute/path/to/workspace-agent",
        "CONTEXT_INDEX_PATH": "/absolute/path/to/workspace-agent/mcp-servers/context-index/index.json"
      }
    }
  }
}

Environment Variables:

With OpenClaw / Claude Desktop

Add to openclaw.json or your MCP client config:

{
  "mcpServers": {
    "context-index": {
      "command": "node",
      "args": ["/path/to/context-index-mcp/index.js"],
      "env": {
        "CONTEXT_INDEX_WORKSPACE": "/path/to/agent/workspace"
      }
    }
  }
}

Data Format

index.json stores an array of objects. file is the unique key (paths relative to CONTEXT_INDEX_WORKSPACE).

{
  "entries": [
    {
      "title": "DB Guide",
      "file": "context/database-guide.md",
      "tags": ["database", "sql"],
      "description": "Schema and query examples",
      "note": "readonly user",
      "updatedAt": "2026-01-01T08:30:00.000Z"
    }
  ]
}

Writes are atomic, preventing corruption on crash. External edits trigger auto-reloads.

Search Scoring

Ranked by weighted keyword matching:

Word-boundary based with prefix tolerance ("deploying" matches tag "deploy"). Entries are penalised if they match fewer query terms.

Why not a Vector DB? For personal agent contexts (low hundreds of files), tag-based keyword matching is faster, fully offline, and highly precise since the agent controls the tags.

The Context File Pattern

Pair this index with plain markdown files that document workflows, credentials, and SOPs. Example context/deploy-to-production.md:

# Deploy Guide
Host: 203.0.113.10
User: deploy

1. SSH in
2. `cd app && git pull origin main`
3. `pm2 restart app`

Instructing Your AI Agent

Add this to your agent's system prompt (e.g. AGENTS.md):

Before performing any task needing workflows, credentials, or tool docs, search the context index:
  mcporter call context-index lookup --args '{"query":"<keywords>"}'

Always index new context files you create immediately:
  mcporter call context-index add --args '{"title":"...", "file":"context/xxx.md", "tags":["tag1"], "description":"..."}'

License

MIT