Glama MCP Registry Scraper

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Scrape Glama's public MCP registry into structured datasets for AI tooling research, vendor discovery, DevRel monitoring, and security due diligence.

What does Glama MCP Registry Scraper do?

Glama MCP Registry Scraper extracts public Model Context Protocol server records from glama.ai.

It can process registry listings, search URLs, category URLs, and individual server detail pages.

The actor saves one dataset row per MCP server.

Each row includes normalized identity fields, registry metadata, developer links, categories, attributes, tools, README text, and score or safety snippets when visible.

Who is it for?

AI platform teams use it to keep a scheduled inventory of MCP servers, compare tool lists across releases, and spot registry changes that affect internal agent platforms.

Developer relations teams use it to find integration partners, track fast-growing MCP categories, and build outreach lists from GitHub, NPM, package, author, and license signals.

MCP builders use it to benchmark competing servers, monitor new servers in their niche, and compare tool counts, README positioning, categories, and score snippets.

Security and governance teams use it to review exposed tool names, package links, repository URLs, licenses, and visible safety or quality signals before approving MCP servers for agent use.

Analysts use it to build searchable MCP market maps and recurring snapshots for trend reports.

Why use it?

Glama changes frequently, and manual directory review is slow.

This actor turns public registry pages into exportable JSON, CSV, Excel, or API data that can be scheduled, diffed, and fed into agent workflows.

Use it for weekly MCP registry snapshots, partner discovery, competitive monitoring, score/safety signal review, or alerting when new servers appear in tracked categories.

What data can I extract?

How much does it cost to scrape Glama MCP registry servers?

This actor uses pay-per-event pricing.

There is a small run-start charge and a per-server result charge.

The default input is intentionally small so first tests stay inexpensive.

Final Store pricing is shown on the Apify actor page after launch.

How to use it

1. Open the actor on Apify.
2. Keep the default Glama directory URL or paste Glama search/category/detail URLs.
3. Choose maxItems.
4. Keep Fetch server detail pages enabled for richer data.
5. Start the run.
6. Export the dataset as JSON, CSV, Excel, XML, or RSS.

Input

{
"startUrls":[
{"url":"https://glama.ai/mcp/servers"}
],
"maxItems":20,
"includeDetails":true,
"maxConcurrency":3
}

Start URL examples

Use the main registry URL:

https://glama.ai/mcp/servers

Use a Glama category URL:

https://glama.ai/mcp/categories/web-scraping

Use a Glama search URL:

https://glama.ai/mcp/servers?query=author%3Amodelcontextprotocol

Use a server detail URL:

https://glama.ai/mcp/servers/modelcontextprotocol/filesystem

Output example

{
"name":"Filesystem MCP Server",
"serverUrl":"https://glama.ai/mcp/servers/modelcontextprotocol/filesystem",
"slug":"modelcontextprotocol/filesystem",
"owner":"modelcontextprotocol",
"repository":"filesystem",
"description":"MCP server for filesystem access",
"authorName":"modelcontextprotocol",
"categories":["File Systems"],
"attributes":["TypeScript","Local"],
"githubUrl":"https://github.com/modelcontextprotocol/servers",
"tools":["read_file","write_file"],
"scrapedAt":"2026-06-27T00:00:00.000Z"
}

Tips for best results

Use Glama search URLs when you need a specific author, keyword, category, or market segment.

Keep concurrency conservative because Glama pages can be slow.

Disable detail pages only when you need a fast, lightweight listing export.

Raise maxItems for scheduled monitoring jobs.

Agent and automation workflows

• Scheduled MCP registry snapshot: run the actor daily or weekly from https://glama.ai/mcp/servers, store each dataset, and diff serverUrl, githubUrl, toolCount, tools, and scoreSignals over time.
• AI platform allowlist review: scrape candidate category or search URLs, then have an internal agent rank servers by repository/package links, licenses, visible tools, and score/safety snippets before a human approval step.
• DevRel partner discovery: monitor categories such as web scraping, databases, browsers, or developer tools and route new GitHub/NPM links to a CRM or outreach queue.
• Security review queue: alert when a tracked author or category adds new tool names, package URLs, or README claims that require governance review.

Integrations

Send results to a warehouse for MCP market intelligence.

Feed selected fields into a vector database for semantic MCP discovery.

Trigger webhooks when new servers appear in tracked categories.

Use Apify integrations to sync records to Google Sheets, Make, Zapier, or a custom API.

API usage with Node.js

Run the actor, wait for it to finish, and consume the dataset records in the same agent script:

import{ ApifyClient }from'apify-client';
const client =newApifyClient({token: process.env.APIFY_TOKEN});
const run =await client.actor('automation-lab/glama-mcp-registry-scraper').call({
startUrls:[{url:'https://glama.ai/mcp/categories/web-scraping'}],
maxItems:50,
includeDetails:true
});
const{ items }=await client.dataset(run.defaultDatasetId).listItems({limit:50});
const partnerCandidates = items.map((server)=>({
name: server.name,
url: server.serverUrl,
github: server.githubUrl,
packages: server.packageUrls,
tools: server.tools,
safetySignals: server.scoreSignals
}));
console.log(JSON.stringify(partnerCandidates,null,2));

API usage with Python

from apify_client import ApifyClient
import os
client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('automation-lab/glama-mcp-registry-scraper').call(run_input={
'startUrls':[{'url':'https://glama.ai/mcp/servers?query=author%3Amodelcontextprotocol'}],
'maxItems':30,
'includeDetails':True,
})
items = client.dataset(run['defaultDatasetId']).list_items(limit=30).items
for server in items:
print({
'name': server.get('name'),
'serverUrl': server.get('serverUrl'),
'tools': server.get('tools'),
'scoreSignals': server.get('scoreSignals'),
})

API usage with cURL

Start a run:

curl-X POST 'https://api.apify.com/v2/acts/automation-lab~glama-mcp-registry-scraper/runs?token=YOUR_TOKEN'\
-H'Content-Type: application/json'\
-d'{"startUrls":[{"url":"https://glama.ai/mcp/servers"}],"maxItems":20,"includeDetails":true}'

Then fetch the resulting dataset with the defaultDatasetId from the run response:

$curl'https://api.apify.com/v2/datasets/DATASET_ID/items?clean=true&format=json&token=YOUR_TOKEN'

Apify MCP usage

This actor is not itself an MCP server and does not expose its own webServerMcpPath.

Agents can still call it through Apify's MCP server as an Apify actor tool, then inspect the returned run and dataset IDs.

Apify MCP server URL:

https://mcp.apify.com/?tools=automation-lab/glama-mcp-registry-scraper

Claude Code setup:

$claude mcp add apify-glama-mcp-registry https://mcp.apify.com/?tools=automation-lab/glama-mcp-registry-scraper

Claude Desktop JSON config:

{
"mcpServers":{
"apify-glama-mcp-registry":{
"url":"https://mcp.apify.com/?tools=automation-lab/glama-mcp-registry-scraper"
}
}
}

Example agent prompts:

Run automation-lab/glama-mcp-registry-scraper for the Glama web-scraping MCP category, read the dataset, and summarize new GitHub repositories plus visible tool names.

Use the Glama MCP Registry Scraper to export 100 developer-tools MCP servers, then group them by package link, license, tool count, and score/safety signals for a platform review.

Find Glama MCP servers from modelcontextprotocol, list their tool counts, GitHub URLs, categories, and any safety snippets, then flag servers that need human governance review.

Monitoring workflow

Schedule the actor daily or weekly.

Store snapshots in Apify datasets or export them to your warehouse.

Compare serverUrl, githubUrl, toolCount, tools, packageUrls, and scoreSignals over time.

Alert your team when a new competitor, integration partner, package, or governance review candidate appears.

Data quality notes

The actor prefers Glama JSON-LD metadata when available.

It falls back to HTML links and text extraction.

Some optional fields may be empty if Glama does not show them on a page.

Tool names are visible only when detail pages expose tool links.

Troubleshooting

If you get fewer records than expected, raise maxItems or add more Glama search/category URLs.

If a run is slow, lower maxConcurrency or disable detail pages.

If a field is empty, verify that it is visible on the public Glama page without logging in.

Legality

This actor extracts public web pages.

You should use the data responsibly and respect Glama's terms, robots guidance, and applicable laws.

Do not use scraped data for spam, abuse, or impersonation.

Related scrapers

Explore other Automation Lab developer-tool and registry scrapers on Apify:

• https://apify.com/automation-lab
• https://apify.com/automation-lab/github-repository-scraper
• https://apify.com/automation-lab/npm-package-scraper

Changelog

Initial version extracts Glama MCP server listing and detail data.

FAQ

Can it scrape every server in the registry?

It is designed for public listing, search, category, and detail pages. Use multiple targeted Glama URLs for larger monitoring workflows.

Does it require login?

No. The MVP uses public Glama pages only.

Does it use a browser?

No. It uses HTTP and Cheerio because Glama exposes useful HTML and JSON-LD in the initial response.

Can it extract tool schemas?

It extracts visible tool names and quality/schema snippets. Deep schema pages can be added in a future version if users need them.

Can I monitor one author?

Yes. Paste a Glama author search URL such as https://glama.ai/mcp/servers?query=author%3Amodelcontextprotocol.

What export formats are supported?

Apify datasets support JSON, CSV, Excel, XML, HTML table, and API access.