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list_metadata_values

Discover available metadata fields and their distinct values for a dataset, enabling accurate construction of metadata-based filters. Use this to identify valid field names and values before querying individual genotypes.

Instructions

List the individual-metadata fields available for a database and their distinct values.

Backed by the endpoint the Gigwa web UI itself uses to populate its metadata-based selection filters, so it works even on builds where get_germplasm_metadata's BrAPI fallback returns no attributes. Use this to discover field names/values before calling filter_individuals_by_metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
variant_set_db_idYesBrAPI variantSetDbId identifying the run (MODULE§project§run) -- copy the exact string from list_variant_sets / list_content, never assemble one by hand: the middle segment is a numeric project index, not the project's name, and a wrong guess fails with an opaque HTTP 500 rather than a clear error.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv1.9.1

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It credits the underlying UI endpoint, explains a key reliability edge case where sibling metadata retrieval fails, and implies a safe read-only operation via the verb 'List'. It could further state that it performs no mutation or side effects, but for a listing tool this is sufficient.

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 concise and front-loaded: the first sentence states the core purpose, and the second adds only high-value context about the backing endpoint and usage relationship. No wasted words.

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 tool has a single well-documented parameter and an output schema, so return values are already covered. The description completes the picture by explaining why the tool exists, when to call it, and how it relates to the broader workflow. Nothing essential is missing.

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%, and the sole parameter 'variant_set_db_id' is already richly documented in the schema with format guidance and a warning about constructing it by hand. The tool description itself adds no additional parameter semantics, so the baseline of 3 applies.

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 opens with a specific verb ('List') and a clear resource ('individual-metadata fields ... and their distinct values'), making the tool's function unambiguous. It also distinguishes itself from related siblings by stating its role in discovering field names/values before 'filter_individuals_by_metadata'.

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 explicitly tells the agent when to use this tool ('before calling filter_individuals_by_metadata') and contrasts it with 'get_germplasm_metadata', noting it works even when the BrAPI fallback returns no attributes. This provides concrete decision guidance for tool selection.

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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