Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Aggregates tools from multiple MCP servers with unified BM25/regex search and deferred loading.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
STORAGE_BACKEND, REDIS_URL, SERVER_CONFIG_PATHYou need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"gateway": {
"command": "uvx",
"args": [
"mcp-orchestration-gateway"
],
"env": {
"STORAGE_BACKEND": "your-value",
"REDIS_URL": "your-value",
"SERVER_CONFIG_PATH": "your-value"
}
}
}
}Run this in your terminal
claude mcp add gateway --env STORAGE_BACKEND=your-value --env REDIS_URL=your-value --env SERVER_CONFIG_PATH=your-value -- uvx mcp-orchestration-gatewayFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"gateway": {
"command": "uvx",
"args": [
"mcp-orchestration-gateway"
],
"env": {
"STORAGE_BACKEND": "your-value",
"REDIS_URL": "your-value",
"SERVER_CONFIG_PATH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"gateway": {
"type": "stdio",
"command": "uvx",
"args": [
"mcp-orchestration-gateway"
],
"env": {
"STORAGE_BACKEND": "your-value",
"REDIS_URL": "your-value",
"SERVER_CONFIG_PATH": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
STORAGE_BACKEND envREDIS_URL envSERVER_CONFIG_PATH envThese settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account rupinder2, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 8 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
A central hub that connects to multiple downstream MCP servers, aggregates their tools, and provides unified access with powerful tool search capabilities.
Built around deferred tool loading — search across all your servers without blowing Claude's context window.
server_name__tool_name formatpip install mcp-orchestrator
# Run as stdio MCP server (for Claude Desktop, Cursor, etc.)
mcp-orchestrator
python -m mcp_orchestrator.main
HTTP Transport:
ORCHESTRATOR_TRANSPORT=http ORCHESTRATOR_PORT=8080 python -m mcp_orchestrator.main
This starts the server on http://localhost:8080/mcp with CORS enabled.
Add downstream MCP servers in server_config.json:
{
"servers": [ { "name": "my-server", "url": "http://localhost:8080/mcp", "transport": "http", "auth_type": "static", "auth_headers": { "Authorization": "Bearer my-token" } }, { "name": "my-stdio-server", "url": "server.py", "transport": "stdio", "command": "uv", "args": ["run", "python", "server.py"] } ] }
The orchestrator provides unified tool search (BM25 by default, regex optional):
# BM25 search (default - natural language)
results = await mcp_client.call_tool("tool_search", { "query": "get weather information", "max_results": 3 })
results = await mcp_client.call_tool("tool_search", { "query": "weather|forecast", "use_regex": true, "max_results": 3 })
┌─────────────────────────────────────────────────────┐
│ MCP Orchestrator │ │ │ │ ┌──────────────────────────────────────────────┐ │ │ │ FastMCP Server │ │ │ │ ┌─────────────┐ ┌──────────────────┐ │ │ │ │ │ tool_search │ │ call_remote_tool │ │ │ │ │ └─────────────┘ └──────────────────┘ │ │ │ └──────────────────────────────────────────────┘ │ │ │ │ ┌──────────┐ ┌──────────┐ ┌──────────────┐ │ │ │ Server │ │ Tool │ │ Storage │ │ │ │ Registry │ │ Search │ │(Memory/Redis)│ │ │ └──────────┘ └──────────┘ └──────────────┘ │ └─────────────────────────────────────────────────────┘ │ ┌───────────────────┼───────────────────┐ ▼ ▼ ▼ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ MCP Svr │ │ MCP Svr │ │ MCP Svr │ │ #1 │ │ #2 │ │ #N │ └─────────┘ └─────────┘ └─────────┘
| Variable | Default | Description |
|---|---|---|
STORAGE_BACKEND | memory | Storage backend (memory or redis) |
REDIS_URL | redis://localhost:6379/0 | Redis connection URL |
MCP_ORCHESTRATOR_TOOL_CACHE_TTL | 300 | Tool schema cache TTL in seconds |
MCP_ORCHESTRATOR_DEFAULT_CONNECTION_MODE | stateless | Default connection mode |
MCP_ORCHESTRATOR_CONNECTION_TIMEOUT | 30.0 | Connection timeout in seconds |
MCP_ORCHESTRATOR_MAX_RETRIES | 3 | Maximum retry attempts |
ORCHESTRATOR_TRANSPORT | stdio | MCP transport (stdio or http) |
ORCHESTRATOR_PORT | 8080 | Port for HTTP transport |
ORCHESTRATOR_HOST | 0.0.0.0 | Host for HTTP transport |
ORCHESTRATOR_LOG_LEVEL | INFO | Logging level |
SERVER_CONFIG_PATH | server_config.json | Path to server configuration file |
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": { "mcp-orchestrator": { "command": "mcp-orchestrator", "env": { "STORAGE_BACKEND": "memory", "ORCHESTRATOR_LOG_LEVEL": "INFO" } } } }
Search for tools using BM25 relevance ranking or regex pattern matching.
@mcp.tool()
async def tool_search( query: str, max_results: int = 3, use_regex: bool = False, ) -> dict: """Search for tools using BM25 or regex.
By default uses BM25 natural language search. Set use_regex=True to search using Python regex patterns instead. """
Discover tools from a registered downstream server.
@mcp.tool()
async def discover_tools( server_name: str, ) -> dict: """Discover tools from a registered server and index them for search.
Returns the list of discovered tools with their schemas. """
Call a tool directly on a downstream MCP server.
@mcp.tool()
async def call_remote_tool( tool_name: str, arguments: Optional[dict] = None, auth_header: Optional[str] = None, ) -> Any: """Call a tool on a downstream server.
Args: tool_name: Namespaced tool name (server_name__tool_name) arguments: Tool arguments auth_header: Optional auth header to override server's configured auth """
The search tools return results in the format expected by Claude's tool search system:
{
"success": true, "tool_references": [ { "type": "tool_reference", "tool_name": "server_name__tool_name" } ], "total_matches": 5, "query": "weather" }
Run the test suite:
uv run pytest
Run with coverage:
uv run pytest --cov=mcp_orchestrator
mcp-orchestrator/
├── src/mcp_orchestrator/ │ ├── init.py │ ├── main.py # Entry point │ ├── models.py # Pydantic models │ ├── mcp_server.py # FastMCP server │ ├── config_loader.py # Config file loader │ ├── server/ │ │ └── registry.py # Server registry │ ├── tools/ │ │ ├── router.py # Tool router │ │ └── search.py # Tool search service │ └── storage/ │ ├── base.py # Storage interface │ ├── memory.py # In-memory backend │ └── redis.py # Redis backend ├── tests/ │ ├── test_registry.py │ ├── test_search.py │ ├── test_storage.py │ ├── test_models.py │ └── test_integration.py ├── server_config.json # Pre-configured downstream servers ├── pyproject.toml ├── README.md └── .env # Environment variables (not committed)
MIT License
Contributions are welcome! Please see CONTRIBUTING.md for guidelines.