MCPFind
Context-efficient MCP tool proxy with semantic search. MCPFind sits between any MCP client and your backend MCP servers, replacing hundreds of tool schemas in the agent's context with just 3 meta-tools (~500 tokens).
Agent (Claude Desktop, Cursor, Claude Code, etc.)
│ Sees only: search_tools, get_tool_schema, call_tool
▼
MCPFind Proxy
├── Vector search over all tool descriptions
├── Per-agent MFU cache for personalized ranking
└── Routes calls to the correct backend server
│
├──▶ Gmail MCP Server
├──▶ GitHub MCP Server
├──▶ Slack MCP Server
└──▶ ... N servers
Why
As MCP toolspaces grow, every tool schema gets dumped into the agent's context:
| Tools | Context tokens | Effect |
|---|---|---|
| 10 | ~2K | Fine |
| 50 | ~10K | Manageable |
| 200 | ~40K | Agent picks wrong tools |
| 1000 | ~200K | Unusable |
MCPFind keeps context at ~500 tokens regardless of how many tools exist behind it. Agents discover tools via semantic search, pull schemas on demand, and call tools through the proxy.
Install
# With uv (recommended)
uv tool install mcpfind
# With pip
pip install mcpfind
No API key needed — MCPFind uses local embeddings by default.
Quick Start
1. Run the setup wizard
The easiest way to get started:
mcpfind setup
This walks you through choosing an embedding provider and adding popular MCP servers (GitHub, Slack, Filesystem, PostgreSQL, Brave Search, Playwright, and more). It generates a mcpfind.toml config file for you.
Or create a config file manually
Create mcpfind.toml:
[proxy]
# Uses local embeddings by default — no API key needed
embedding_provider = "local" # or "openai"
embedding_model = "all-MiniLM-L6-v2" # or "text-embedding-3-small" for openai
mfu_boost_weight = 0.15
mfu_persist = true
default_max_results = 5
[[servers]]
name = "github"
command = "uvx"
args = ["mcp-server-github"]
env = { GITHUB_TOKEN = "${GITHUB_TOKEN}" }
[[servers]]
name = "filesystem"
command = "uvx"
args = ["mcp-server-filesystem", "/path/to/allowed/dir"]
2. Verify your setup
# List all tools discovered from your backend servers
mcpfind list-tools --config mcpfind.toml
# Test semantic search
mcpfind search "create a pull request" --config mcpfind.toml
3. Run the proxy
mcpfind serve --config mcpfind.toml
This starts MCPFind as a stdio MCP server. Point your MCP client at it instead of individual servers.
Adding MCP Servers
Each backend server is a [[servers]] entry in your config file:
[[servers]]
name = "gmail" # Unique name (used in search results and call_tool)
command = "uvx" # Command to launch the server
args = ["mcp-gmail"] # Arguments passed to the command
env = { GMAIL_TOKEN = "${GMAIL_TOKEN}" } # Environment variables (supports ${VAR} expansion)
Examples
GitHub:
[[servers]]
name = "github"
command = "uvx"
args = ["mcp-server-github"]
env = { GITHUB_TOKEN = "${GITHUB_TOKEN}" }
Filesystem:
[[servers]]
name = "filesystem"
command = "uvx"
args = ["mcp-server-filesystem", "/home/user/documents"]
Slack:
[[servers]]
name = "slack"
command = "uvx"
args = ["mcp-server-slack"]
env = { SLACK_BOT_TOKEN = "${SLACK_BOT_TOKEN}" }
Custom / local server:
[[servers]]
name = "my-server"
command = "python"
args = ["-m", "my_mcp_server"]
env = { MY_API_KEY = "${MY_API_KEY}" }
Client Configuration
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"mcpfind": {
"command": "mcpfind",
"args": ["serve", "--config", "/path/to/mcpfind.toml"],
"env": {
"GITHUB_TOKEN": "ghp_..."
}
}
}
}
Claude Code
Add to your .mcp.json:
{
"mcpServers": {
"mcpfind": {
"command": "mcpfind",
"args": ["serve", "--config", "/path/to/mcpfind.toml"]
}
}
}
Cursor
Add to your MCP settings:
{
"mcpServers": {
"mcpfind": {
"command": "mcpfind",
"args": ["serve", "--config", "/path/to/mcpfind.toml"]
}
}
}
How It Works
MCPFind exposes exactly 3 tools to the agent:
-
search_tools— Find relevant tools by natural language query (e.g., "send an email"). Returns tool names, servers, and descriptions ranked by semantic similarity + usage frequency. -
get_tool_schema— Pull the full input schema for a specific tool before calling it. Keeps schemas out of context until actually needed. -
call_tool— Execute a tool on a backend server. MCPFind validates and routes the call to the correct server.
Agent workflow
Agent: search_tools("send an email")
→ [{"server": "gmail", "name": "send_email", "score": 0.94}, ...]
Agent: get_tool_schema(server="gmail", tool="send_email")
→ {"type": "object", "properties": {"to": ..., "subject": ..., "body": ...}}
Agent: call_tool(server="gmail", tool="send_email", arguments={...})
→ "Email sent!"
MFU Cache
MCPFind tracks which tools each agent uses most frequently. Frequently used tools get a ranking boost in search results via the mfu_boost_weight config option (default: 0.15). This means 85% of the ranking comes from semantic similarity and 15% from usage frequency.
Set mfu_persist = true to save usage data across restarts (stored in mfu.db).
Configuration Reference
[proxy]
embedding_provider = "local" # "local" (default) or "openai"
embedding_model = "all-MiniLM-L6-v2" # Model name (provider-specific)
mfu_boost_weight = 0.15 # Frequency boost weight (0.0-1.0)
mfu_persist = true # Persist usage data to SQLite
default_max_results = 5 # Default number of search results
[[servers]]
name = "server-name" # Required: unique identifier
command = "command" # Required: executable to launch
args = ["arg1", "arg2"] # Optional: command arguments
env = { KEY = "value" } # Optional: environment variables (${VAR} expansion supported)
CLI Reference
# Interactive setup wizard
mcpfind setup
# Start the proxy server (stdio MCP transport)
mcpfind serve --config mcpfind.toml
# List all discovered tools from backend servers
mcpfind list-tools --config mcpfind.toml
# Test semantic search
mcpfind search "query" --config mcpfind.toml --max-results 10
Development
# Clone and install
git clone https://github.com/jcgs2503/mcp-lens.git
cd mcp-lens
uv sync
# Run tests
uv run pytest -v
# Lint and format
uv run ruff check .
uv run black --check .