pgs-mcp

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assess_match_qc

Read-onlyIdempotent

Assess how well polygenic scores matched a target genotype dataset in pgsc_calc by reporting variant match rate, weight coverage, strand handling, exclusions, and per-chromosome coverage for a single score or whole run.

Instructions

Assess how well polygenic scores matched a target genotype dataset in pgsc_calc.

Works for a single score or a whole run. Returns raw metrics (variant match rate, weight coverage, strand handling, exclusions, per-chromosome coverage), structural warnings and descriptive observations. Large runs return a compact index of every score; request specific pgs_ids for full detail. Interpret with the guide at pgs://guides/match-qc.

Input Schema

Name	Required	Description
`pgs_ids`	No	Optional PGS IDs (e.g. PGS000004) or full accessions to report on. Omit for every score in the file.
`log_path`	No	Optional path to the variant-level match log, e.g. `{sampleset}_log.csv.gz`. Enables weight, chromosome, indel and match-type metrics. The first read of a large log takes about a minute per few GB; it is then cached.
`summary_path`	Yes	Path to the pgsc_calc match summary, e.g. `{sampleset}_summary.csv`.

Output Schema

Name	Required	Description
`scores`	Yes	Full detail for selected scores.
`dataset`	Yes
`log_used`	Yes
`n_scores`	Yes
`warnings`	Yes
`chromosomes`	Yes	Run-wide coverage over score-variant pairs; a shared variant counts per score. Null without a log, or when specific scores were requested.
`detail_note`	No	Set when detail was limited; explains how to get more.
`score_index`	Yes
`genome_builds`	Yes
`n_pipeline_fail`	Yes
`n_pipeline_pass`	Yes
`inferred_settings`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 29, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context: raw metrics and warnings are returned, large runs produce a compact score index, and specific pgs_ids produce full detail.

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 front-loaded with the core purpose, then efficiently adds scope, return behavior, large-run handling, and the interpretation guide. Every sentence carries useful information and there is no filler.

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?

Given the output schema and annotations, the description covers enough for an agent to call the tool correctly: purpose, scope, return behavior, large-run behavior, and guide reference. The main gap is the absence of any mention of the sibling render_qc_report.

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 100%, so the baseline is 3. The description adds meaning beyond the schema for pgs_ids by explaining the tradeoff: large runs return a compact index, while requesting specific pgs_ids yields full detail.

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 description uses a specific verb and resource: assess how well polygenic scores matched a target genotype dataset in pgsc_calc. It is clear what the tool does, but it never mentions or distinguishes itself from the sibling render_qc_report, so it falls short of the top score.

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?

The description implies usage by saying it works for a single score or a whole run, and it points to the match-qc guide for interpretation. However, it gives no explicit when-not guidance and does not explain how to choose this tool versus render_qc_report.

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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