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render_qc_report

Generate a Markdown QC report for one PGS score or an entire run, summarizing match metrics, chromosome coverage, and per-score details to flag variant-matching issues.

Instructions

Build a Markdown match QC report for one score or a whole run.

A single score gets a focused report; several scores get a run overview, distributions, chromosome coverage, a table of every score and detail sections. For large runs, pass output_path to write the report to disk rather than returning it.

Input Schema

NameRequiredDescriptionDefault
pgs_idsNoOptional PGS IDs (e.g. PGS000004) or full accessions to report on. Omit for every score in the file.
log_pathNoOptional 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.
output_pathNoOptional .md path to write the report to instead of returning it.
summary_pathYesPath to the pgsc_calc match summary, e.g. `{sampleset}_summary.csv`.
interpretationNoOptional Markdown interpretation to place at the top of the report, written after reading the assess_match_qc output and the interpretation guide. It is placed under an Interpretation heading; a leading heading of your own with that name is merged into it.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With annotations only declaring readOnlyHint=false and openWorldHint=false, the description adds real context: that output is Markdown, that report structure varies by the number of scores, and that the report can be written to disk rather than returned. It does not discuss the cost implications of the underlying data beyond what the schema says about log caching, but the key behavioral tradeoff (return vs write) is disclosed.

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?

Three short sentences, front-loaded with the purpose and with zero filler. The enumeration of overview/distributions/coverage/table is mildly verbose but does usefully signal what the agent will get back, so it earns its place.

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?

An output schema exists, so return details need not be restated, and the description covers input roles, the single-vs-batch behavioral split, and the disk-output escape hatch for large runs. Nothing an agent needs in order to call this correctly appears to be 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%, so the schema already explains pgs_ids, log_path, output_path, summary_path and interpretation. The description restates the score-count semantics for pgs_ids and the write-to-disk behavior of output_path but adds no syntax, format, or constraint details beyond the schema, so baseline 3 applies.

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 opens with a specific verb and artifact ('Build a Markdown match QC report') and scopes it to 'one score or a whole run', which is concrete and distinguishable. It never names the sibling assess_match_qc as the contrasting tool, so the boundary between the two is only implied by the parameter text, not stated up front.

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?

It gives clear conditional guidance: a single score yields a focused report, several scores yield a run overview, and large runs should use output_path instead of returning the report. It does not explicitly state when to prefer this over assess_match_qc or that this tool should only follow an assessment, leaving that inference to the interpretation parameter.

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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