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Glama

get_taxid_info

Look up taxonomic information for an NCBI taxonomy ID in eggNOG, returning JSON details and the download URL for full taxonomy data.

Instructions

Look up taxonomic information for an NCBI taxonomy ID.

Provides guidance on retrieving taxonomic information for a given NCBI taxonomy ID from the eggNOG database. The full taxonomic data is available in the downloadable e7.taxid_info.tsv.gz file.

Parameters

taxid : int NCBI taxonomy ID. Examples: - 9606 (Homo sapiens) - 4932 (Saccharomyces cerevisiae) - 44689 (Danio rerio)

Returns

str JSON string with taxonomic lookup information and download URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taxidYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose that the result is a JSON string containing taxonomic lookup information and a download URL, which is useful behavioral context. It says nothing about permissions, rate limits, or behavior for unknown taxids, leaving real gaps for a tool with zero 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.

Conciseness3/5

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

The first sentence is well front-loaded, but the second sentence largely restates it ("Provides guidance on retrieving taxonomic information..."), and the Parameters/Returns blocks duplicate what the input and output schemas already express. There is avoidable repetition, though the examples earn their place.

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?

With one required parameter, an output schema present, and no annotations, the description covers what an agent needs to invoke the tool: the meaning of taxid, concrete examples, and the shape of the return. Only edge-case behavior and the bulk-download alternative remain underspecified.

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?

Schema description coverage is 0% — the taxid property has no description in the schema — so the description must compensate, and it does: it defines taxid as an int NCBI taxonomy ID and supplies three concrete examples (9606, 4932, 44689). That is meaningfully more than the schema offers, though it doesn't note valid ranges or behavior on invalid IDs.

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 opening line states a specific verb and resource: "Look up taxonomic information for an NCBI taxonomy ID." This is clearly distinguishable from sibling tools like get_protein or get_orthologs, which operate on different resources. However, no sibling is named, so it does not earn a 5.

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?

Usage is only implied: the note that "the full taxonomic data is available in the downloadable e7.taxid_info.tsv.gz file" hints at an alternative path (bulk download via list_downloads) without stating when to prefer this tool over that one. There is no explicit when-to-use or when-not-to-use guidance, so it lands at implied usage rather than clear context.

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