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ashishdevthakur3-max

Firecrawl MCP Server

firecrawl_deep_research

Analyze complex research questions by crawling multiple web sources and generating comprehensive LLM-based analysis.

Instructions

Conduct deep web research on a query using intelligent crawling, search, and LLM analysis.

Best for: Complex research questions requiring multiple sources, in-depth analysis. Not recommended for: Simple questions that can be answered with a single search; when you need very specific information from a known page (use scrape); when you need results quickly (deep research can take time). Arguments:

  • query (string, required): The research question or topic to explore.

  • maxDepth (number, optional): Maximum recursive depth for crawling/search (default: 3).

  • timeLimit (number, optional): Time limit in seconds for the research session (default: 120).

  • maxUrls (number, optional): Maximum number of URLs to analyze (default: 50). Prompt Example: "Research the environmental impact of electric vehicles versus gasoline vehicles." Usage Example:

{
  "name": "firecrawl_deep_research",
  "arguments": {
    "query": "What are the environmental impacts of electric vehicles compared to gasoline vehicles?",
    "maxDepth": 3,
    "timeLimit": 120,
    "maxUrls": 50
  }
}

Returns: Final analysis generated by an LLM based on research. (data.finalAnalysis); may also include structured activities and sources used in the research process.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe query to research
maxDepthNoMaximum depth of research iterations (1-10)
timeLimitNoTime limit in seconds (30-300)
maxUrlsNoMaximum number of URLs to analyze (1-1000)

Implementation Reference

  • The handler for the 'firecrawl_deep_research' tool in the request-handling switch statement.
    case 'firecrawl_deep_research': {
      if (!args || typeof args !== 'object' || !('query' in args)) {
        throw new Error('Invalid arguments for firecrawl_deep_research');
      }
    
      try {
        const researchStartTime = Date.now();
        safeLog('info', `Starting deep research for query: ${args.query}`);
    
        const response = await client.deepResearch(
          args.query as string,
          {
            maxDepth: args.maxDepth as number,
            timeLimit: args.timeLimit as number,
            maxUrls: args.maxUrls as number,
            // @ts-expect-error Extended API options including origin
            origin: 'mcp-server',
          },
          // Activity callback
          (activity) => {
            safeLog(
              'info',
              `Research activity: ${activity.message} (Depth: ${activity.depth})`
            );
          },
          // Source callback
          (source) => {
            safeLog(
              'info',
              `Research source found: ${source.url}${source.title ? ` - ${source.title}` : ''}`
            );
          }
        );
    
        // Log performance metrics
        safeLog(
          'info',
          `Deep research completed in ${Date.now() - researchStartTime}ms`
        );
    
        if (!response.success) {
          throw new Error(response.error || 'Deep research failed');
        }
    
        // Format the results
        const formattedResponse = {
          finalAnalysis: response.data.finalAnalysis,
          activities: response.data.activities,
          sources: response.data.sources,
        };
    
        return {
          content: [
  • src/index.ts:589-616 (registration)
    Definition of the 'firecrawl_deep_research' tool metadata and schema documentation.
    const DEEP_RESEARCH_TOOL: Tool = {
      name: 'firecrawl_deep_research',
      description: `
    Conduct deep web research on a query using intelligent crawling, search, and LLM analysis.
    
    **Best for:** Complex research questions requiring multiple sources, in-depth analysis.
    **Not recommended for:** Simple questions that can be answered with a single search; when you need very specific information from a known page (use scrape); when you need results quickly (deep research can take time).
    **Arguments:**
    - query (string, required): The research question or topic to explore.
    - maxDepth (number, optional): Maximum recursive depth for crawling/search (default: 3).
    - timeLimit (number, optional): Time limit in seconds for the research session (default: 120).
    - maxUrls (number, optional): Maximum number of URLs to analyze (default: 50).
    **Prompt Example:** "Research the environmental impact of electric vehicles versus gasoline vehicles."
    **Usage Example:**
    \`\`\`json
    {
      "name": "firecrawl_deep_research",
      "arguments": {
        "query": "What are the environmental impacts of electric vehicles compared to gasoline vehicles?",
        "maxDepth": 3,
        "timeLimit": 120,
        "maxUrls": 50
      }
    }
    \`\`\`
    **Returns:** Final analysis generated by an LLM based on research. (data.finalAnalysis); may also include structured activities and sources used in the research process.
    `,
      inputSchema: {
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's a time-intensive operation ('deep research can take time'), involves LLM analysis, and returns a final analysis plus structured activities and sources. It doesn't mention rate limits, authentication needs, or error conditions, but covers the core behavior adequately.

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?

The description is well-structured with clear sections (purpose, guidelines, arguments, examples) and front-loads the core purpose. While comprehensive, some redundancy exists (parameter details duplicated from schema). Every section serves a purpose, but it could be more concise by avoiding schema duplication.

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

Completeness4/5

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

Given 4 parameters with 100% schema coverage but no annotations and no output schema, the description does well by explaining the tool's behavioral characteristics, usage context, and return values. It covers what the tool does, when to use it, and what it returns, though it could benefit from more detail on error conditions or limitations.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description repeats parameter information already in the schema (names, types, defaults) without adding significant semantic context beyond what's documented in the schema properties. The 'Arguments' section essentially duplicates schema information.

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 the tool's purpose with specific verbs ('conduct deep web research', 'intelligent crawling, search, and LLM analysis') and distinguishes it from siblings by contrasting with 'scrape' for known pages and 'search' for simple questions. It explicitly names the resource (web research) and scope (complex questions requiring multiple sources).

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?

The description provides explicit guidance with dedicated 'Best for' and 'Not recommended for' sections, naming specific alternatives like 'scrape' and 'search'. It clearly states when to use (complex research questions requiring multiple sources) and when not to use (simple questions, known pages, quick results), directly addressing sibling tool differentiation.

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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