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ilhankilic

YaparAI MCP Server

by ilhankilic

get_job_status

Check the status of a generation job and retrieve the result URL once completed. Ideal for monitoring long-running video or music tasks without using credits.

Instructions

Check the status of a generation job.

Use this to check on long-running jobs (especially video and music) or to retrieve the result URL of a completed job. No credits charged.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYesThe job ID returned from a generate call

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The get_job_status async function that implements the tool logic. It creates a client and delegates to client.get_job().
    async def get_job_status(
        job_id: str,
    ) -> dict:
        """
        Check the status of a generation job.
    
        Use this to check on long-running jobs (especially video and music)
        or to retrieve the result URL of a completed job.
        No credits charged.
    
        Args:
            job_id: The job ID returned from a generate call
    
        Returns:
            Dict with job_id, status (pending/processing/succeeded/failed),
            mode, result_url (when done), error_message (if failed),
            and created_at timestamp.
        """
        client = YaparAIClient()
        return await client.get_job(job_id)
  • The YaparAIClient.get_job() method that makes the actual HTTP GET request to /v1/public/jobs/{job_id}.
    async def get_job(self, job_id: str) -> dict:
        """Get job status and result."""
        return await self._request("GET", f"/v1/public/jobs/{job_id}")
  • Registration of get_job_status as an MCP tool via mcp.tool(get_job_status) on the FastMCP server.
    mcp.tool(get_job_status)
  • Import of get_job_status from yaparai.tools.jobs into the server module.
    from yaparai.tools.jobs import get_job_status
  • The docstring serves as the schema definition: accepts job_id (str), returns a dict with job_id, status, mode, result_url, error_message, created_at.
    async def get_job_status(
        job_id: str,
    ) -> dict:
        """
        Check the status of a generation job.
    
        Use this to check on long-running jobs (especially video and music)
        or to retrieve the result URL of a completed job.
        No credits charged.
    
        Args:
            job_id: The job ID returned from a generate call
    
        Returns:
            Dict with job_id, status (pending/processing/succeeded/failed),
            mode, result_url (when done), error_message (if failed),
            and created_at timestamp.
        """
        client = YaparAIClient()
        return await client.get_job(job_id)
Behavior3/5

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

No annotations provided, so the description carries the full burden. It adds the 'no credits charged' behavioral detail but does not disclose other aspects like rate limits or side effects.

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?

Three concise sentences with no extraneous information, effectively front-loading the main purpose and key usage points.

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?

The description covers the main function, usage context, and cost implications; presence of an output schema means return details are not needed here, making it complete.

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

Parameters4/5

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

With 100% schema coverage, the parameter 'job_id' is already documented, but the description adds value by clarifying it's 'returned from a generate call', providing context beyond the schema.

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 checks the status of a generation job, specifies it for long-running jobs (video/music) and retrieving result URLs, which distinguishes it from sibling generation tools.

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?

Explicitly states when to use (for long-running jobs, to get result URL) and notes no credits charged, but does not cover when not to use or provide alternatives.

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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