Anysite MCP Server

The entire web is your database. Your AI assistant is the query interface.

A Model Context Protocol server that lets Claude, Cursor, ChatGPT, and any MCP client query structured data from LinkedIn, Instagram, Twitter/X, Reddit, YouTube, SEC EDGAR, Y Combinator, Crunchbase, and any URL on the web — through five universal tools.

What's new in 2.0

The server no longer ships 60+ individual tools. Instead, it exposes five universal meta-tools and discovers the underlying endpoints dynamically from the AnySite OpenAPI spec. This means:

• Every new endpoint added to the AnySite API is available the next time the server starts — no package update required.
• The agent learns the surface through discover(source, category) rather than from a static tool catalog.
• Pagination, filtering and exports are handled in-process via the cache: one execute() call, then unlimited get_page / query_cache / export_data calls without re-paying API credits.

The five tools

Sources surfaced through discover: LinkedIn, Instagram, Twitter/X, Reddit, YouTube, SEC EDGAR, Y Combinator, Crunchbase, DuckDuckGo, the universal web parser, plus AI parsers for GitHub, Amazon, Google Maps, G2, BuiltWith, Apify, GLEIF, Newegg and more. New endpoints appear automatically as soon as they ship on the platform — no package update required.

Skills — ready-made workflows

The MCP server gives raw access. Anysite Skills package that access into named workflows you summon by topic, so the assistant runs a proven pattern instead of improvising. Built for Claude Code (also works in Cursor / Windsurf via the same plugin format).

Full catalog (17 skills, including VC analyst, influencer discovery, audience analysis, content analytics, trend analysis): github.com/anysiteio/agent-skills.

Install

In Claude Code:

/plugin marketplace add https://github.com/anysiteio/agent-skills
/plugin install anysite-lead-generation@anysite-skills

You can install one skill or the whole marketplace. The MCP server connection above is the only prerequisite.

Local development

git clone https://github.com/anysiteio/anysite-mcp.git
cd anysite-mcp
npm install
npm run build:tsc
ANYSITE_ACCESS_TOKEN=... node build/server.js

Inspect the tools interactively:

npm run inspector

Project layout

src/
├── server.ts          stdio entry (CLI bin)
├── index.ts           Smithery TypeScript runtime adapter
├── loader.ts          fetches and parses the OpenAPI spec
├── registry.ts        in-memory source/category/endpoint registry
├── client.ts          HTTP client with retry/timeout
├── cache.ts           cache_key → items
├── query.ts           in-memory filter / sort / aggregate
├── export.ts          json / jsonl / csv writers
├── tools.ts           the five meta-tool handlers
├── tool-schemas.ts    MCP tool input schemas (zod + JSON Schema)
├── instructions.ts    instructions text for the LLM
├── config.ts          env-derived constants
└── types.ts           shared types

License

MIT

Quick start

Two ways to connect. Remote MCP is the recommended path for everyday use — managed cache, OAuth, no infrastructure. Self-hosted is for development or when you want the server running locally over stdio.

Option 1: Remote MCP with OAuth (recommended)

Hosted at https://mcp.anysite.io/mcp. Works with Claude Desktop, Claude Code, Cursor, Cline, Windsurf, ChatGPT, and any MCP-compliant client.

Step 1: Sign up

Create an account at anysite.io. The MCP30 promo covers your first month of MCP Unlimited ($30/month, 6 req/min fair-use, no credit counting). Credit-based plans (Starter $49 / Growth $200 / Scale $300 / Pro $549 / Enterprise from $1,199) also include MCP access; see docs.anysite.io/mcp-server/overview.

Step 2: Connect your client

1. Settings → Connectors → Add Custom Connector
2. URL: https://mcp.anysite.io/mcp
3. Click Connect, authorize in the browser
4. Required: Settings → Capabilities → Tool access → Tools already loaded

The last step is critical. Claude Desktop's "Load tools when needed" mode uses name-based matching that fails on generic names like discover and execute. If you skip it, the tools exist but the model will not reliably call them.

claude mcp add --transport http anysite "https://mcp.anysite.io/mcp?api_key=YOUR_KEY"
claude mcp list   # should show: anysite connected

The URL carries your API key — treat it as a secret.

Edit ~/.cursor/mcp.json (global) or .cursor/mcp.json in the project root:

{
  "mcpServers": {
    "anysite": {
      "url": "https://mcp.anysite.io/mcp?api_key=YOUR_KEY",
      "transport": "http"
    }
  }
}

Anysite implements MCP 1.0 over Streamable HTTP with OAuth 2.0. Point any compliant client at https://mcp.anysite.io/mcp and authenticate via OAuth or ?api_key=YOUR_KEY.

Step 3: Verify

Ask the assistant:

What MCP tools do you have from anysite?

It should list exactly: discover, execute, get_page, query_cache, export_data.

Step 4: Run a query

Use anysite to find 20 CTOs at Series B fintech startups in New York,
then show me the ones with AI or ML in their headline.

The assistant runs discover on LinkedIn, picks the right search endpoint, calls execute, then narrows server-side with query_cache.

Option 2: Self-hosted (npm, stdio)

For local development, custom integrations, or when you want the server in-process. Distributed as @anysiteio/mcp on npm. Runs over stdio; cache lives in-process for 7 days.

Install

npm install -g @anysiteio/mcp

Or run on demand with npx -y @anysiteio/mcp.

Configure your client

{
  "mcpServers": {
    "anysite": {
      "command": "npx",
      "args": ["-y", "@anysiteio/mcp"],
      "env": {
        "ANYSITE_ACCESS_TOKEN": "your_token_here"
      }
    }
  }
}

That is the entire configuration. Only ANYSITE_ACCESS_TOKEN is required — create one at app.anysite.io.

Usage pattern

discover("linkedin", "search")
  → endpoints: ["users", "companies", "posts", ...] with params

execute("linkedin", "search", "users", { keywords: "CTO AI" })
  → { items: [...first 10], total: 50, cache_key: "abc..." , next_offset: 10 }

get_page("abc...", offset=10)            # next page, free
query_cache("abc...", { conditions: [{ field: "location", op: "contains", value: "San Francisco" }] })
export_data("abc...", "csv")             # dump everything to a CSV file

Example: find decision makers and export to CSV

1. discover("linkedin", "search")
2. execute("linkedin", "search", "users", { keywords: "CTO", company_keywords: "AI" })
3. query_cache(cache_key, { conditions: [{ field: "location", op: "contains", value: "Berlin" }] })
4. export_data(cache_key, "csv")

The agent figures out the parameter names on its own from discover.

Configuration