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AdvaitR7

Firecrawl MCP Multiple Keys

by AdvaitR7

firecrawl_agent

Autonomously browses the web to find and extract structured data based on your natural language prompt, returning a job ID for async result polling.

Instructions

Autonomous web research agent. This is a separate AI agent layer that independently browses the internet, searches for information, navigates through pages, and extracts structured data based on your query. You describe what you need, and the agent figures out where to find it.

How it works: The agent performs web searches, follows links, reads pages, and gathers data autonomously. This runs asynchronously - it returns a job ID immediately, and you poll firecrawl_agent_status to check when complete and retrieve results.

IMPORTANT - Async workflow with patient polling:

  1. Call firecrawl_agent with your prompt/schema → returns job ID immediately

  2. Poll firecrawl_agent_status with the job ID to check progress

  3. Keep polling for at least 2-3 minutes - agent research typically takes 1-5 minutes for complex queries

  4. Poll every 15-30 seconds until status is "completed" or "failed"

  5. Do NOT give up after just a few polling attempts - the agent needs time to research

Expected wait times:

  • Simple queries with provided URLs: 30 seconds - 1 minute

  • Complex research across multiple sites: 2-5 minutes

  • Deep research tasks: 5+ minutes

Best for: Complex research tasks where you don't know the exact URLs; multi-source data gathering; finding information scattered across the web; extracting data from JavaScript-heavy SPAs that fail with regular scrape. Not recommended for:

  • Single-page extraction when you have a URL (use firecrawl_scrape, faster and cheaper)

  • Web search (use firecrawl_search first)

  • Interactive page tasks like clicking, filling forms, login, or navigating JS-heavy SPAs (use firecrawl_scrape + firecrawl_interact)

  • Extracting specific data from a known page (use firecrawl_scrape with JSON format)

Arguments:

  • prompt: Natural language description of the data you want (required, max 10,000 characters)

  • urls: Optional array of URLs to focus the agent on specific pages

  • schema: Optional JSON schema for structured output

Prompt Example: "Find the founders of Firecrawl and their backgrounds" Usage Example (start agent, then poll patiently for results):

{
  "name": "firecrawl_agent",
  "arguments": {
    "prompt": "Find the top 5 AI startups founded in 2024 and their funding amounts",
    "schema": {
      "type": "object",
      "properties": {
        "startups": {
          "type": "array",
          "items": {
            "type": "object",
            "properties": {
              "name": { "type": "string" },
              "funding": { "type": "string" },
              "founded": { "type": "string" }
            }
          }
        }
      }
    }
  }
}

Then poll with firecrawl_agent_status every 15-30 seconds for at least 2-3 minutes.

Usage Example (with URLs - agent focuses on specific pages):

{
  "name": "firecrawl_agent",
  "arguments": {
    "urls": ["https://docs.firecrawl.dev", "https://firecrawl.dev/pricing"],
    "prompt": "Compare the features and pricing information from these pages"
  }
}

Returns: Job ID for status checking. Use firecrawl_agent_status to poll for results.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsNo
promptYes
schemaNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes async behavior, polling workflow, and expected wait times. Annotations provide readOnlyHint=false and openWorldHint=true; description adds context on how the agent navigates and extracts data without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is verbose but well-structured with sections (How it works, Important, etc.). Every sentence adds value; could be slightly trimmed but remains clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers async workflow, polling, when to use, examples, and return value. References sibling status tool. Complete for a complex async tool without output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema coverage, the description explains each parameter: prompt (natural language, max length), urls (optional focus), schema (structured output). Provides examples and usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Autonomous web research agent' and explains it browses the internet to find and extract structured data. It distinguishes from siblings like firecrawl_scrape (single page extraction) and firecrawl_search (web search).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends for complex research tasks and multi-source data gathering. Lists not-recommended scenarios with alternative tools (firecrawl_scrape, firecrawl_search, firecrawl_interact).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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