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Firecrawl MCP Server

by ampcome-mcps

firecrawl_deep_research

Conduct deep web research on complex queries using intelligent crawling, search, and LLM analysis to generate comprehensive insights from multiple sources.

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 main handler function for the firecrawl_deep_research tool. Validates input arguments, initializes the Firecrawl client, calls the deepResearch method with query and optional parameters (maxDepth, timeLimit, maxUrls), handles activity and source logging callbacks, processes the response, and returns the final analysis as text content.
    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: [
            {
              type: 'text',
              text: trimResponseText(formattedResponse.finalAnalysis),
            },
          ],
          isError: false,
        };
      } catch (error) {
        const errorMessage =
          error instanceof Error ? error.message : String(error);
        return {
          content: [{ type: 'text', text: trimResponseText(errorMessage) }],
          isError: true,
        };
      }
    }
  • Defines the Tool object for firecrawl_deep_research, including name, detailed description, and inputSchema for validating parameters like query (required), maxDepth, timeLimit, and maxUrls.
    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: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'The query to research',
          },
          maxDepth: {
            type: 'number',
            description: 'Maximum depth of research iterations (1-10)',
          },
          timeLimit: {
            type: 'number',
            description: 'Time limit in seconds (30-300)',
          },
          maxUrls: {
            type: 'number',
            description: 'Maximum number of URLs to analyze (1-1000)',
          },
        },
        required: ['query'],
      },
    };
  • src/index.ts:963-973 (registration)
    Registers the firecrawl_deep_research tool (as DEEP_RESEARCH_TOOL) in the list of available tools returned by the ListToolsRequestSchema handler.
      tools: [
        SCRAPE_TOOL,
        MAP_TOOL,
        CRAWL_TOOL,
        CHECK_CRAWL_STATUS_TOOL,
        SEARCH_TOOL,
        EXTRACT_TOOL,
        DEEP_RESEARCH_TOOL,
        GENERATE_LLMSTXT_TOOL,
      ],
    }));
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 discloses important behavioral traits: it mentions the tool 'can take time' (performance characteristic), describes the output ('Final analysis generated by an LLM', 'may also include structured activities and sources'), and implies resource-intensive operations through parameter explanations. It doesn't mention rate limits or authentication needs, but provides substantial context beyond basic functionality.

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 (Best for, Not recommended for, Arguments, examples) and front-loads the core purpose. While comprehensive, some sections like the detailed usage example could be more concise. Most sentences earn their place by providing distinct value.

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 the tool's complexity (deep research with crawling/search/LLM analysis), no annotations, and no output schema, the description does well by explaining the return format, providing usage guidelines, and detailing parameters. It could benefit from more explicit behavioral constraints (e.g., rate limits, error conditions) but is largely complete for agent understanding.

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 adds value by providing default values for optional parameters (maxDepth: 3, timeLimit: 120, maxUrls: 50) and contextualizes parameters in the research process, but doesn't significantly expand on the schema's parameter descriptions beyond these defaults.

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 mentioning when not to use it (e.g., 'when you need very specific information from a known page (use scrape)'). It explicitly names the resource (web research) and differentiates from simpler alternatives.

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 sections: 'Best for:' lists complex research questions requiring multiple sources and in-depth analysis, 'Not recommended for:' specifies three clear scenarios including alternatives (e.g., 'use scrape'), and distinguishes from sibling tools like 'scrape' and 'search'. This gives comprehensive when/when-not/alternatives information.

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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