Skip to main content
Glama
mamertofabian

ElevenLabs MCP Server

get_voiceover_history

Retrieve your ElevenLabs voiceover job history or get details for a specific job by providing its ID.

Instructions

Get voiceover job history. Optionally specify a job ID for a specific job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNoOptional job ID to get details for a specific job

Implementation Reference

  • Tool registration including name, description, and input schema definition.
        name="get_voiceover_history",
        description="Get voiceover job history. Optionally specify a job ID for a specific job.",
        inputSchema={
            "type": "object",
            "properties": {
                "job_id": {
                    "type": "string",
                    "description": "Optional job ID to get details for a specific job"
                }
            },
            "required": []
        }
    )
  • Handler function that fetches voiceover job history from database (all jobs or specific job_id), serializes to JSON, and returns as text content.
    elif name == "get_voiceover_history":
        try:
            job_id = arguments.get("job_id")
            if job_id:
                job = await self.db.get_job(job_id)
                if not job:
                    return [types.TextContent(
                        type="text",
                        text=json.dumps({"error": "Job not found"}, indent=2)
                    )]
                jobs = [job]
            else:
                jobs = await self.db.get_all_jobs()
    
            # Convert jobs to JSON
            jobs_data = [job.to_dict() for job in jobs]
            return [types.TextContent(
                type="text",
                text=json.dumps(jobs_data, indent=2)
            )]
            
        except Exception as e:
            return [types.TextContent(
                type="text",
                text=json.dumps({"error": str(e)}, indent=2)
            )]
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves history but doesn't describe return format, pagination, error handling, authentication needs, rate limits, or whether it's read-only. The description is minimal and lacks essential behavioral context for a tool with no annotation coverage.

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 extremely concise with two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second clarifies the optional parameter usage, making it efficient and well-structured.

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

Completeness2/5

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

Given no annotations, no output schema, and a simple input schema, the description is incomplete. It doesn't explain what 'voiceover job history' entails (e.g., list of jobs, statuses, metadata), return values, or behavioral traits like safety or performance. For a tool with minimal structured data, the description should provide more context to be fully helpful.

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?

The input schema has 100% description coverage, with the parameter 'job_id' documented as 'Optional job ID to get details for a specific job'. The description adds minimal value by restating this as 'Optionally specify a job ID for a specific job', which doesn't provide additional semantics beyond the schema. Baseline 3 is appropriate given high schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose as 'Get voiceover job history' with the verb 'Get' and resource 'voiceover job history'. It distinguishes between retrieving general history versus specific job details, though it doesn't explicitly differentiate from sibling tools like 'get_audio_file' or 'list_voices'.

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

Usage Guidelines3/5

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

The description provides implied usage guidance by mentioning the optional job ID parameter for retrieving a specific job, which suggests when to use this tool versus alternatives. However, it lacks explicit guidance on when to use this tool over siblings like 'get_audio_file' or 'list_voices', and no exclusions or prerequisites are stated.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mamertofabian/elevenlabs-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server