ena-biosamples-mcp

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check_sample_metadata

Validate ENA/BioSamples sample metadata for completeness and correct formatting, flagging missing required fields or optional project-specific attributes.

Instructions

Check whether an ENA/BioSamples sample's metadata is complete and well-formed.

Always checks the fields ENA requires on every sample (organism, collection date, geographic location). Flags fields that are absent, filled with an INSDC missing-value term (e.g. "not collected"), or badly formatted (collection date not ISO 8601).

Args: accession: BioSamples accession, e.g. "SAMEA7658521". extra_fields: Additional attributes to require, e.g. ["sex", "breed", "tissue"] for a livestock (FAANG-style) project.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "accession": "<accession>"
    },
    "name": "check_sample_metadata"
  }
}

Input Schema

Name	Required	Description	Default
`accession`	Yes
`extra_fields`	No

Output Schema

Name	Required	Description	Default
`fields`	Yes
`passed`	Yes
`summary`	Yes
`accession`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 11, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and does so partly: it discloses that the check always evaluates ENA's mandatory fields, defines what counts as a failure (absent, INSDC missing-value term, bad formatting), and implies this is a read-only validator by nature. It stops short of explicitly stating permissions, that the sample need not exist/be reachable, or that validation is non-mutating.

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 narrative is front-loaded and the Args block is compact and skimmable. Slight redundancy between the prose summary and the Args section, but every sentence adds information and no padding is present.

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?

For a two-parameter read-only validator with an output schema, the description covers purpose, semantics, and the validation criteria an agent needs to interpret results. The output schema handles return format, so the only missing element is explicit guidance on sibling selection and any preconditions on sample accessibility.

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

Parameters5/5

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: 'accession' is grounded with a concrete example ('SAMEA7658521'), and 'extra_fields' is explained as additional required attributes with a domain-specific example. Both parameters' intent is fully recoverable from the description alone.

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?

States a specific verb and resource ('Check whether an ENA/BioSamples sample's metadata is complete and well-formed') and immediately enumerates what the check covers (required fields, missing-value terms, ISO 8601 date format). This distinguishes it cleanly from siblings like get_biosample (retrieval) and count_records (aggregation), none of which validate metadata.

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

Usage Guidelines4/5

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

The description gives clear positive context (validate a BioSamples sample's metadata, with an explicit livestock/FAANG example motivating extra_fields), so an agent knows when this tool applies. However it never states when NOT to use it or names an alternative among the siblings, so it falls short of a 5.

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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