# Research MCP Server — 10 Tools for AI Agents (`dami_studio/research-mcp-server`) Actor MCP server giving any AI agent (Claude, ChatGPT, Cursor) ten fast research tools in one endpoint: arXiv, Reddit, GitHub, Hacker News, OpenAlex, Stack Exchange, Wikipedia, CoinGecko markets, GDELT news, and Telegram. Enable Standby and point your MCP client at the actor's URL. - **URL**: https://apify.com/dami\_studio/research-mcp-server.md - **Developed by:** [Dami's Studio](https://apify.com/dami_studio) (community) - **Categories:** AI, Developer tools, MCP servers - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Research MCP Server — 10 Tools for AI Agents **One MCP endpoint that gives any AI agent ten fast research tools.** Point your MCP client (Claude Desktop, Claude Code, ChatGPT, Cursor, or any Model Context Protocol app) at this Actor's URL and your agent can instantly search papers, code, news, forums, crypto markets, and more — no per-tool setup, no glue code. > This is a **hosted MCP server**, not a scraper you run once. Connect once and the ten tools > show up in your agent automatically via MCP tool discovery. --- ### The 10 tools | Tool | What it does | |---|---| | `search_arxiv` | Search arXiv papers (title, abstract, authors, categories, links) | | `search_reddit` | Search Reddit posts across all of Reddit or one subreddit | | `search_github` | Search GitHub repositories and users | | `search_hacker_news` | Search Hacker News stories and comments | | `search_openalex` | Search the OpenAlex scholarly graph (works, authors, venues) | | `search_stackexchange` | Search Stack Overflow / Stack Exchange questions | | `search_wikipedia` | Search Wikipedia and pull clean article extracts | | `crypto_markets` | Live cryptocurrency market data (CoinGecko) | | `search_news` | Search Google News for real publisher articles | | `telegram_channel` | Read public Telegram channel posts | Each tool returns clean, structured JSON — ready for an agent to reason over. --- ### How to connect This Actor runs as a **Standby MCP server** over HTTP (Streamable HTTP transport). Use its Standby URL as the MCP server endpoint, authenticated with your Apify token: ```` https://dami-studio--research-mcp-server.apify.actor/ Authorization: Bearer \ ```` **Claude Desktop / Cursor (mcp config):** ```json { "mcpServers": { "research": { "url": "https://dami-studio--research-mcp-server.apify.actor/", "headers": { "Authorization": "Bearer " } } } } ```` Then ask your agent things like *"search arXiv for recent papers on retrieval-augmented generation"* or *"what's trending on Hacker News about Rust?"* — it picks the right tool automatically. > **First call may be slow.** The server and each tool warm up on first use (Standby cold > start); subsequent calls are fast. If a first call times out, just retry. *** ### Why this exists AI agents are far more useful when they can pull live external data. Wiring up ten separate research APIs (each with its own auth, rate limits, and response shape) is tedious. This server collapses all of that into **one URL and one token** — ten consistent, structured research tools your agent can call out of the box. Great for: research assistants, market/competitive monitoring agents, coding agents that need to check GitHub/Stack Overflow/HN, and any Claude/ChatGPT workflow that benefits from live data. *** ### Notes - Built on the official Model Context Protocol SDK (`McpServer` + Streamable HTTP, stateless per request). - Each tool is a thin, low-latency proxy to a dedicated, hardened source Actor — so results are clean and the toolset is easy to extend. - Public sources only; no login or scraping of private data. # Actor input Schema ## `info` (type: `string`): This actor is an MCP server. Enable Standby for this actor, then connect your AI agent / MCP client to the actor's Standby URL. It exposes 10 research tools (search\_arxiv, search\_reddit, search\_github, search\_hacker\_news, search\_openalex, search\_stackexchange, search\_wikipedia, crypto\_markets, search\_news, telegram\_channel). A normal (non-Standby) run just prints these usage notes. ## Actor input object example ```json {} ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("dami_studio/research-mcp-server").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 = {} # Run the Actor and wait for it to finish run = client.actor("dami_studio/research-mcp-server").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 '{}' | apify call dami_studio/research-mcp-server --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dami_studio/research-mcp-server", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Research MCP Server — 10 Tools for AI Agents", "description": "MCP server giving any AI agent (Claude, ChatGPT, Cursor) ten fast research tools in one endpoint: arXiv, Reddit, GitHub, Hacker News, OpenAlex, Stack Exchange, Wikipedia, CoinGecko markets, GDELT news, and Telegram. Enable Standby and point your MCP client at the actor's URL.", "version": "1.0", "x-build-id": "Ye842hQ7oGZ69ue6H" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dami_studio~research-mcp-server/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dami_studio-research-mcp-server", "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/dami_studio~research-mcp-server/runs": { "post": { "operationId": "runs-sync-dami_studio-research-mcp-server", "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/dami_studio~research-mcp-server/run-sync": { "post": { "operationId": "run-sync-dami_studio-research-mcp-server", "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", "properties": { "info": { "title": "How to use", "type": "string", "description": "This actor is an MCP server. Enable Standby for this actor, then connect your AI agent / MCP client to the actor's Standby URL. It exposes 10 research tools (search_arxiv, search_reddit, search_github, search_hacker_news, search_openalex, search_stackexchange, search_wikipedia, crypto_markets, search_news, telegram_channel). A normal (non-Standby) run just prints these usage notes." } } }, "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 } } } } } } } } } } ```