# MCP Scout — find the right MCP server for any task (`01010101/mcp-scout`) Actor Describe your task in plain language and get the best matching MCP servers, ranked and deduplicated across registries, with ready-to-paste install commands and JSON configs. - **URL**: https://apify.com/01010101/mcp-scout.md - **Developed by:** [Naoki](https://apify.com/01010101) (community) - **Categories:** AI, Agents, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 task searches This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## MCP Scout — find the right MCP server for any task Describe what you need in plain language. MCP Scout searches multiple MCP registries (the official Model Context Protocol registry and PulseMCP), deduplicates and ranks the results, and returns the best matches **with ready-to-paste setup configs** for Claude Code, Claude Desktop, Cursor, and any MCP-compatible client. Built for both humans and AI agents: an agent can call this actor mid-task to discover and wire up the capability it's missing. ### Why not just browse a registry? - **One query, all registries** — results are merged and deduplicated across sources. - **Ranked for usability, not just relevance** — hosted remotes (zero install) and actively maintained servers rank higher; GitHub stars break ties. - **Zero-friction setup** — every result includes the exact `claude mcp add …` command and the JSON `mcpServers` snippet, for remote, npm, and PyPI variants. ### Input ```json { "query": "send messages to slack channels", "maxResults": 5, "requireRemote": false } ```` - `query` (required) — plain-language description of the task. - `maxResults` — how many ranked matches to return (default 5). - `requireRemote` — only return servers with a hosted endpoint (usable immediately, no local install). ### Output One dataset item per match: ```json { "rank": 1, "name": "ai.smithery/slack", "description": "Send messages, manage channels, ...", "score": 9.5, "why": "matches: slack, messages; hosted remote available (no install needed); 1200 GitHub stars", "remotes": [{ "type": "streamable-http", "url": "https://..." }], "setup": [ { "kind": "remote", "claudeCodeCommand": "claude mcp add --transport http slack https://...", "jsonConfig": { "mcpServers": { "slack": { "type": "http", "url": "https://..." } } } } ] } ``` The full response (including `totalCandidates` and any per-source errors) is stored as the run's `OUTPUT` key-value record. ### Use cases - **Agents**: discover and self-wire missing capabilities mid-task. - **Developers**: skip the registry-crawling; get the config snippet directly. - **Teams**: audit what's available before building a custom MCP server. # Actor input Schema ## `query` (type: `string`): Describe what you need in plain language, e.g. 'send messages to Slack channels' or 'query a Postgres database'. ## `maxResults` (type: `integer`): How many ranked matches to return (1-25). ## `requireRemote` (type: `boolean`): Only return servers with a hosted remote endpoint (usable immediately, no local install). ## Actor input object example ```json { "query": "scrape a website and extract structured data", "maxResults": 5, "requireRemote": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "query": "scrape a website and extract structured data" }; // Run the Actor and wait for it to finish const run = await client.actor("01010101/mcp-scout").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "query": "scrape a website and extract structured data" } # Run the Actor and wait for it to finish run = client.actor("01010101/mcp-scout").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "query": "scrape a website and extract structured data" }' | apify call 01010101/mcp-scout --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=01010101/mcp-scout", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Scout — find the right MCP server for any task", "description": "Describe your task in plain language and get the best matching MCP servers, ranked and deduplicated across registries, with ready-to-paste install commands and JSON configs.", "version": "0.1", "x-build-id": "tFCDHf79zCbohitzs" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/01010101~mcp-scout/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-01010101-mcp-scout", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/01010101~mcp-scout/runs": { "post": { "operationId": "runs-sync-01010101-mcp-scout", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/01010101~mcp-scout/run-sync": { "post": { "operationId": "run-sync-01010101-mcp-scout", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "query" ], "properties": { "query": { "title": "Task description", "type": "string", "description": "Describe what you need in plain language, e.g. 'send messages to Slack channels' or 'query a Postgres database'." }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 25, "type": "integer", "description": "How many ranked matches to return (1-25).", "default": 5 }, "requireRemote": { "title": "Hosted remotes only", "type": "boolean", "description": "Only return servers with a hosted remote endpoint (usable immediately, no local install).", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```