# MCP Server Discovery & Quality Scanner (`express_kingfisher/mcp-server-discovery-scanner`) Actor Programmatic Model Context Protocol (MCP) server registry scanner. Scrapes Glama MCP registry, audits remote/local configurations, checks JSON schemas, and calculates custom quality and developer scores. - **URL**: https://apify.com/express\_kingfisher/mcp-server-discovery-scanner.md - **Developed by:** [Prince Raj](https://apify.com/express_kingfisher) (community) - **Categories:** MCP servers, Developer tools, Automation - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results 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 Server Discovery & Quality Scanner The most comprehensive Model Context Protocol (MCP) server registry scanner and quality auditor. Programmatically crawls Glama's MCP registry, audits local/remote configurations, checks required environment variables, and scores every MCP server by utility and developer readiness. --- ### 🚀 Features - **Multi-Source Discovery:** Fetches live registry data from Glama's official MCP index. - **Smart Quality Auditing:** Dynamically calculates a comprehensive quality score (0-100) based on hosting attributes, environment schema completion, open-source metrics, and recent updates. - **Parameter Schema Normalization:** Converts complex JSON environment and tool schemas into flat, easily searchable, developer-friendly formats. - **AI Opportunity Scoring:** Helps identify high-potential gaps in the MCP ecosystem. --- ### 📥 Sample Input ```json { "query": "whisper", "maxResults": 50, "includeTools": true, "includeQualityScore": true } ```` *** ### 📤 Sample Output (Dataset Item) ```json { "id": "loa8bep52w", "name": "whisper-mcp", "namespace": "skroes", "description": "MCP server that provides a transcribe_audio tool to convert voice messages from channels into text using OpenAI Whisper.", "repoUrl": "https://github.com/skroes/whisper-mcp", "attributes": ["hosting:local-only"], "hosting": "Local Only", "requiredEnvVars": ["OPENAI_API_KEY"], "qualityScore": 85, "lastUpdated": "2026-05-17T12:00:00.000Z" } ``` *** ### 💰 Suggested Pricing - **Free Trial:** First 20 results. - **Pay Per Result:** $0.002 per scanned MCP server. # Actor input Schema ## `query` (type: `string`): Filter MCP servers by name, namespace, description, or attributes (e.g. 'whisper', 'postgres', 'local-only'). Leave empty to fetch all. ## `maxResults` (type: `integer`): Maximum number of MCP servers to scan. ## `includeTools` (type: `boolean`): If true, audits and normalizes JSON schema definitions for tools and parameters. ## `includeQualityScore` (type: `boolean`): Applies a heuristic quality formula based on hosting options, descriptions, license, and repository metrics. ## Actor input object example ```json { "maxResults": 50, "includeTools": true, "includeQualityScore": true } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("express_kingfisher/mcp-server-discovery-scanner").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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("express_kingfisher/mcp-server-discovery-scanner").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 express_kingfisher/mcp-server-discovery-scanner --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=express_kingfisher/mcp-server-discovery-scanner", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "MCP Server Discovery & Quality Scanner", "description": "Programmatic Model Context Protocol (MCP) server registry scanner. Scrapes Glama MCP registry, audits remote/local configurations, checks JSON schemas, and calculates custom quality and developer scores.", "version": "1.0", "x-build-id": "v6IMeoT7xuKTHJi2q" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/express_kingfisher~mcp-server-discovery-scanner/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-express_kingfisher-mcp-server-discovery-scanner", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/express_kingfisher~mcp-server-discovery-scanner/runs": { "post": { "operationId": "runs-sync-express_kingfisher-mcp-server-discovery-scanner", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/express_kingfisher~mcp-server-discovery-scanner/run-sync": { "post": { "operationId": "run-sync-express_kingfisher-mcp-server-discovery-scanner", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Search query", "type": "string", "description": "Filter MCP servers by name, namespace, description, or attributes (e.g. 'whisper', 'postgres', 'local-only'). Leave empty to fetch all." }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of MCP servers to scan.", "default": 50 }, "includeTools": { "title": "Include tools data", "type": "boolean", "description": "If true, audits and normalizes JSON schema definitions for tools and parameters.", "default": true }, "includeQualityScore": { "title": "Calculate Quality Score", "type": "boolean", "description": "Applies a heuristic quality formula based on hosting options, descriptions, license, and repository metrics.", "default": true } } }, "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 } } } } } } } } } } ```