Firecrawl Agent MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| FIRECRAWL_API_KEY | Yes | Your Firecrawl API key from https://www.firecrawl.dev/ |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| agent_executeB | Execute Firecrawl Agent to search, navigate, and gather data from the web. The agent autonomously finds and extracts information based on your prompt. Waits for completion and returns results. Use this for immediate results. |
| agent_startA | Start a Firecrawl Agent job asynchronously. Returns a job ID immediately without waiting for completion. Use this for long-running research tasks. Poll with agent_status to check progress. |
| agent_statusA | Check the status of an asynchronous Firecrawl Agent job. Returns current status, progress, and results if completed. Job results are available for 24 hours after completion. |
| scrapeA | Scrape a single URL and extract content in various formats (markdown, html, links, screenshot). Use this for simple single-page scraping without AI agent capabilities. |
| searchA | Search the web and scrape the results. Returns scraped content from multiple search results. Use this for finding and extracting data from multiple sources at once. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose with no ambiguity: agent_execute for immediate agent tasks, agent_start for async jobs, agent_status for job monitoring, scrape for single URLs, and search for web searches. The descriptions explicitly differentiate use cases, preventing misselection.
All tool names follow a consistent snake_case pattern with clear verb_noun or noun_verb structures (e.g., agent_execute, agent_start, scrape, search). There are no deviations in naming conventions, making the set predictable and readable.
With 5 tools, the server is well-scoped for web data extraction and agent tasks. Each tool earns its place by covering distinct aspects: synchronous and asynchronous agent execution, job monitoring, single-page scraping, and multi-source search. This count is neither too sparse nor bloated.
The tool surface covers core workflows for web scraping and agent-based data gathering, including start, execute, status, scrape, and search operations. A minor gap exists in lacking explicit tools for managing or canceling async jobs, but agents can work around this by using the provided tools effectively.