Skip to main content
Glama

get_metadata

Retrieve metadata from gnomAD genomic datasets to understand dataset structure, version details, and available fields for analysis.

Instructions

[gnomAD API] Retrieve metadata (v2/v3/v4) Args: dataset (str): gnomAD dataset ID (gnomad_r4/gnomad_r3/gnomad_r2_1) Returns: dict: metadata

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYes
reference_genomeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function `get_metadata` is defined in `server.py` and registered as an MCP tool using `@mcp.tool()`. It takes dataset and reference genome as arguments and returns the result of `run_query_with_metadata`.
    def get_metadata(
        dataset: str,
        reference_genome: str
    ) -> dict:
        """
        [gnomAD API] Retrieve metadata (v2/v3/v4)
        Args:
            dataset (str): gnomAD dataset ID (gnomad_r4/gnomad_r3/gnomad_r2_1)
        Returns:
            dict: metadata
        """
        variables = {
            'dataset': dataset,
            'reference_genome': reference_genome,
        }
        return run_query_with_metadata('meta', variables)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the tool retrieves metadata but doesn't disclose behavioral traits like whether it's read-only (implied by 'retrieve'), authentication needs, rate limits, error handling, or what the returned dict contains. The description is minimal and lacks context beyond the basic operation.

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 concise and structured with clear sections for Args and Returns, making it easy to parse. However, it could be more front-loaded by starting with a more informative summary instead of the bracketed '[gnomAD API]'. The brevity is efficient but borders on under-specification.

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

Completeness3/5

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

Given the tool has an output schema (returns a dict), the description doesn't need to detail return values. However, with no annotations, 2 parameters (one undocumented), and sibling tools that might overlap, the description is incomplete. It covers the basic operation but lacks context on usage, behavior, and full parameter semantics, making it minimally adequate.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It documents the 'dataset' parameter with examples (gnomad_r4/gnomad_r3/gnomad_r2_1), which adds value, but it omits the 'reference_genome' parameter entirely. With 2 parameters and incomplete coverage, the description fails to fully explain parameter meanings beyond what the schema provides.

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

Purpose3/5

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

The description states the tool retrieves metadata from the gnomAD API, which is a clear purpose. However, it doesn't specify what kind of metadata (e.g., dataset version info, sample counts, quality metrics) or distinguish it from sibling tools that retrieve specific data types like variant or gene information. The mention of 'v2/v3/v4' versions is helpful but vague.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, such as needing a dataset ID, or differentiate it from sibling tools like get_variant_info or get_gene_info, which might retrieve overlapping metadata. Usage is implied only by the tool name and parameters.

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/koido/gnomad-mcp'

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