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Glama

validate_file

Check bioinformatics files (FASTA, FASTQ, VCF, BED, GFF, CSV, TSV, JSON) for validity, record count, and issues like truncation, length mismatches, or broken headers.

Instructions

Validate a bioinformatics file (FASTA, FASTQ, VCF, BED, GFF, CSV, TSV, JSON).

Returns validity, record count, and up to 5 issues. Catches truncation, FASTQ length mismatches, broken VCF headers, ragged tables.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
formatNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose the return content (validity, record count, up to 5 issues) and the failure classes it detects (truncation, FASTQ length mismatch, broken VCF headers, ragged tables). It omits other operational traits such as behavior on very large files, read scope, or whether the file is read fully or streamed, so the burden is only partly met.

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 short sentences, front-loaded with the action and scope, then the return shape, then concrete failure modes. No filler and nothing buried.

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?

An output schema exists, so return formatting needn't be spelled out, and the description still usefully names the key result fields and detected error classes. The remaining gap is guidance about scale and the format parameter's role, which an agent would need to call this confidently.

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 coverage is 0%, so the description must compensate, and it does not. The format list hints at accepted values for the format parameter but never explains whether format is an optional override, whether it is auto-detected (the schema default is an empty string), or why the caller would set it; the required path parameter is completely unexplained.

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?

Specific verb (validate) plus resource (bioinformatics file) with an explicit format list, so the agent knows exactly what it does. It is implicitly distinct from the run-oriented siblings, but the description never acknowledges the nearest neighbor, check_run, so the reader must infer the file-vs-run distinction.

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?

The description states what the tool does but never says when to reach for it instead of check_run or the other run tools. There is no stated precondition, no exclusion, and no alternative named, so the agent gets no routing guidance.

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