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Firecrawl Agent MCP Server

agent_status

Check the status, progress, and results of a Firecrawl Agent web data extraction job. Returns current job status and completed data for 24 hours after processing.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdYesThe job ID returned from agent_start

Implementation Reference

  • MCP tool handler for 'agent_status'. Destructures 'jobId' from input arguments, calls firecrawl.getAgentStatus(jobId), and returns the result as formatted JSON text content.
    case 'agent_status': {
      const { jobId } = args as { jobId: string };
    
      const result = await firecrawl.getAgentStatus(jobId);
    
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(result, null, 2),
          },
        ],
      };
    }
  • src/server.ts:121-135 (registration)
    Registration of the 'agent_status' tool in the TOOLS array, including name, description, and input schema definition.
    {
      name: 'agent_status',
      description:
        '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.',
      inputSchema: {
        type: 'object',
        properties: {
          jobId: {
            type: 'string',
            description: 'The job ID returned from agent_start',
          },
        },
        required: ['jobId'],
      },
    },
  • Input schema validation for 'agent_status' tool, requiring a 'jobId' string parameter.
    inputSchema: {
      type: 'object',
      properties: {
        jobId: {
          type: 'string',
          description: 'The job ID returned from agent_start',
        },
      },
      required: ['jobId'],
    },
  • Core helper function getAgentStatus in FirecrawlClient that performs the API GET request to /v1/agent/{jobId} to fetch the agent job status and handles errors.
    async getAgentStatus(jobId: string): Promise<FirecrawlAgentJob> {
      try {
        const response = await fetch(`${this.apiBase}/v1/agent/${jobId}`, {
          method: 'GET',
          headers: {
            'Authorization': `Bearer ${this.apiKey}`,
          },
        });
    
        const data = await response.json() as any;
    
        if (!response.ok) {
          return {
            id: jobId,
            status: 'failed',
            error: data.error || `HTTP ${response.status}: ${response.statusText}`,
          };
        }
    
        return {
          id: jobId,
          status: data.status,
          data: data.data,
          error: data.error,
          creditsUsed: data.creditsUsed,
          progress: data.progress,
        };
      } catch (error) {
        return {
          id: jobId,
          status: 'failed',
          error: error instanceof Error ? error.message : 'Unknown error',
        };
      }
    }
  • TypeScript interface defining the structure of the agent job status response (output schema for the tool).
    export interface FirecrawlAgentJob {
      id: string;
      status: 'pending' | 'running' | 'completed' | 'failed';
      data?: any;
      error?: string;
      creditsUsed?: number;
      progress?: number;
    }
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: the tool returns status/progress/results, results are temporary (24-hour retention), and it works with asynchronous jobs. However, it doesn't mention error handling, rate limits, authentication requirements, or what happens after 24 hours. The description adds value but lacks comprehensive behavioral context.

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

Conciseness5/5

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

The description is perfectly concise with two sentences that each earn their place: the first states the purpose and return values, the second provides critical behavioral context about result retention. No wasted words, and information is front-loaded appropriately.

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 moderate complexity (status checking with temporal constraints), no annotations, and no output schema, the description does well but has gaps. It explains what the tool does, when to use it, and key behavioral constraints. However, without an output schema, it doesn't detail the structure of returned status/progress/results, leaving some uncertainty about the response format.

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%, with the single parameter 'jobId' well-documented in the schema. The description adds minimal value beyond the schema by mentioning 'job ID returned from agent_start,' which provides context but no additional semantic meaning. This meets the baseline of 3 when schema coverage is high.

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 specific action ('Check the status'), target resource ('asynchronous Firecrawl Agent job'), and scope ('current status, progress, and results if completed'). It distinguishes from siblings like agent_start (which initiates jobs) and agent_execute (likely executes synchronously) by focusing on status monitoring of existing jobs.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: after starting an asynchronous job with agent_start, to monitor its progress and retrieve results. It implies an alternative (waiting for completion) but doesn't explicitly name when NOT to use it or compare with other status-checking methods. The 24-hour retention period provides useful temporal guidance.

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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