# Model Context Protocol MCP Servers Intel Scraper (`primesieve/mcp-servers-intel`) Actor

Discover and monitor Model Context Protocol (MCP) servers, tools, input schemas, use counts, and deployment configs from Smithery and official MCP registries. Zero proxy needed.

- **URL**: https://apify.com/primesieve/mcp-servers-intel.md
- **Developed by:** [Prime Sieve](https://apify.com/primesieve) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 mcp server records

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Model Context Protocol (MCP) Servers Intelligence Scraper

Extract and monitor live **Model Context Protocol (MCP)** servers, tool definitions, tool schemas, remote deployment URLs, and usage analytics across official and community MCP registries.

Zero proxies needed. Built for AI developers, tool orchestrators, AI agent builders, and researchers tracking the MCP ecosystem.

***

### 🚀 Why Track MCP Servers?

The **Model Context Protocol (Open Standard)** is the emerging interface standard connecting AI agents (Claude Code, Cursor, Windsurf, LangGraph, AutoGen, OpenAI Agents SDK) to external APIs, developer tools, and databases.

With thousands of MCP servers published across registries:

- **Discover Tools**: Find ready-to-use MCP integrations for PostgreSQL, GitHub, Notion, Brave Search, Jira, Stripe, and custom APIs.
- **Inspect Schemas**: Extract full parameter input/output JSON schemas for agent tool calling.
- **Track Ecosystem Trends**: Monitor verified servers, use counts, new releases, and remote HTTP/SSE deployment endpoints.

***

### ⚡ Features

- 🔍 **Keyword Search**: Search across thousands of MCP servers by name, tag, or technology.
- 🛠️ **Deep Tool Inspection**: Automatically fetch tool list, parameter schemas, and descriptions for each server.
- 🌐 **Remote Endpoints**: Extract live streamable HTTP/SSE endpoints ready to plug into your MCP client config.
- 📊 **Rich Dataset Views**: Pre-configured table views for Quick Overview and Deep Tool/Schema inspection.
- ⚡ **Blazing Fast & Zero-Proxy**: Pure HTTP API fetching without browser overhead or IP blocking.

***

### 📥 Input Parameters

| Parameter | Type | Default | Description |
|---|---|---|---|
| `query` | String | `""` | Search keyword (e.g. `github`, `database`, `slack`, `search`). Leave blank for top servers. |
| `maxResults` | Integer | `50` | Maximum number of MCP servers to scrape. |
| `fetchDetails` | Boolean | `true` | Extract full tool list, tool schemas, and connection details for each server. |
| `verifiedOnly` | Boolean | `false` | Only return verified MCP servers. |
| `remoteOnly` | Boolean | `false` | Only return servers that support direct remote streamable HTTP/SSE. |

***

### 📤 Output Data Format

```json
{
  "qualifiedName": "brave",
  "displayName": "Brave Search",
  "description": "Search the web with Brave's independent index...",
  "homepage": "https://search.brave.com",
  "verified": true,
  "remote": true,
  "useCount": 87579,
  "score": 0.05,
  "deploymentUrl": "https://brave.run.tools",
  "toolsCount": 7,
  "toolsSummary": "brave_web_search, brave_news_search, brave_image_search, brave_video_search, brave_local_search, brave_llm_context, brave_place_search, brave_summarizer",
  "tools": [
    {
      "name": "brave_web_search",
      "description": "Performs web searches using Brave Search API...",
      "inputSchema": { ... }
    }
  ],
  "connections": [ ... ],
  "scrapedAt": "2026-09-17T09:00:00.000Z"
}
```

***

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Built with high-frequency rigor by [Prime Sieve](https://github.com/primesievecoder).

# Actor input Schema

## `query` (type: `string`):

Optional keyword to search across MCP servers (e.g. 'github', 'brave', 'postgres', 'weather', 'search'). Leave empty to fetch top servers.

## `maxResults` (type: `integer`):

Maximum number of MCP servers to extract.

## `fetchDetails` (type: `boolean`):

When enabled, fetches full tool definitions, input/output schemas, and connection settings for each server.

## `verifiedOnly` (type: `boolean`):

If enabled, only extracts servers that are verified.

## `remoteOnly` (type: `boolean`):

If enabled, only extracts servers that support remote SSE / Streamable-HTTP connections.

## Actor input object example

```json
{
  "maxResults": 50,
  "fetchDetails": true,
  "verifiedOnly": false,
  "remoteOnly": false
}
```

# Actor output Schema

## `qualifiedName` (type: `string`):

Unique identifier for the MCP server

## `displayName` (type: `string`):

Human-readable name of the MCP server

## `description` (type: `string`):

Overview of server functionality

## `toolsCount` (type: `string`):

Total number of tools provided by the server

## `toolsSummary` (type: `string`):

List of available tool names

## `useCount` (type: `string`):

Total install/use counter

## `verified` (type: `string`):

True if verified by registry

## `remote` (type: `string`):

True if supports streamable HTTP/SSE remote connections

## `homepage` (type: `string`):

Link to documentation or repository

## `deploymentUrl` (type: `string`):

Live remote URL endpoint for connecting AI agents

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("primesieve/mcp-servers-intel").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("primesieve/mcp-servers-intel").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 primesieve/mcp-servers-intel --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,primesieve/mcp-servers-intel"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/YlaEVcGrCiPqAggZv/builds/erOjrpPyxRvRSFhUp/openapi.json