Crabeye Bridge MCP server

Crabeye MCP Bridge

One MCP connection for all your tools — with discovery, namespacing, and execution policies.

Every MCP server you add to your AI assistant means another connection, another set of tool definitions injected into the context window, and no way to search or control them centrally. Wire up ten servers with a hundred tools each and your assistant is burning tokens on a thousand tool schemas before the conversation even starts — most of which it will never call.

crabeye-mcp-bridge consolidates all your upstream MCP servers behind a single STDIO interface and exposes exactly two tools to the assistant: searchtools and runtool. Tools from every server are discovered, namespaced, and indexed at startup, but none of them touch the context window until the assistant actually searches for them. You can have a thousand tools ready to go without bloating the context, with fuzzy search to find them and per-tool execution policies to control what runs freely, what needs approval, and what is blocked.

Quick start

The fastest way to get started is with init, which discovers your MCP client configs and sets up the bridge automatically:

npx @crabeye-ai/crabeye-mcp-bridge init

This scans for config files from Claude Desktop, Cursor, VS Code Copilot, Windsurf, Zed, Cline, Roo Code, opencode, and Continue.dev, lets you pick which ones to use, and optionally injects the bridge entry. After that, just run npx @crabeye-ai/crabeye-mcp-bridge — no --config flag needed.

opencode and Continue.dev are surfaced as detect-only: their config schemas don't fit the rename-and-inject pipeline, so init prints a manual snippet for them instead of rewriting the file.

To undo, run npx @crabeye-ai/crabeye-mcp-bridge restore.

Manual setup

If you prefer to set things up manually, say your MCP client config looks like this today:

{
  "mcpServers": {
    "linear": {
      "command": "npx",
      "args": ["-y", "@anthropic/linear-mcp-server"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@anthropic/github-mcp-server"],
      "env": {
        "GITHUB_TOKEN": "ghp_..."
      }
    }
  }
}

First, store your secrets in the encrypted credential store:

crabeye-mcp-bridge credential set github-pat ghp_abc123

Then rename mcpServers to upstreamMcpServers, add the bridge, and replace hardcoded tokens with ${credential:key} references:

{
  "mcpServers": {
    "bridge": {
      "command": "npx",
      "args": ["-y", "@crabeye-ai/crabeye-mcp-bridge", "--config", "/path/to/this/file.json"]
    }
  },
  "upstreamMcpServers": {
    "linear": {
      "command": "npx",
      "args": ["-y", "@anthropic/linear-mcp-server"]
    },
    "github": {
      "command": "npx",
      "args": ["-y", "@anthropic/github-mcp-server"],
      "env": {
        "GITHUB_TOKEN": "${credential:github-pat}"
      }
    }
  }
}

That's it. Your AI assistant now has access to all tools from all configured servers through a single connection. The bridge automatically excludes itself from mcpServers to avoid recursion, so pointing --config at the same file is safe.

The bridge also reads upstreamServers (shorthand), servers (VS Code Copilot), and context_servers (Zed) as input keys. See docs/configuration.md for the full priority order and self-exclusion rules.

Shortened. The full README is on GitHub.