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Folklore Clinical Variant Interpretation MCP

Classify or interpret a germline variant under ACMG/AMP

classify_germline_variant
Read-onlyIdempotent

Classify one GRCh38 germline nuclear SNV or simple indel smaller than 50 bp under ACMG/AMP. Accepts genomic, coding or protein HGVS, coordinates, SPDI, rsID or a returned canonical key. Returns normalized identity, automated classification, applied criteria, ClinVar assertions, population frequencies, annotations and source provenance when available. Ambiguous input returns candidates for selection.

Input Schema

NameRequiredDescriptionDefault
queryYesOne germline nuclear SNV or simple indel to resolve and interpret; accepted forms include coordinates, genomic/coding/protein HGVS, SPDI, rsID, or a returned Folklore canonical_key in GRCh38:chrN:position:REF:ALT form.
assemblyNoReference genome assembly. Folklore currently accepts GRCh38 only.GRCh38

Output Schema

NameRequiredDescriptionDefault
resultYes
record_urlYes
adapter_errorYes
contract_versionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, closed-world and non-destructive, so the safety profile is carried. The description adds genuinely useful behavior beyond that: it enumerates the returned payload (normalized identity, classification, applied criteria, ClinVar assertions, frequencies, provenance) and discloses the ambiguity fallback that returns candidate list rather than a result.

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 sentences, each carrying distinct information: accepted scope, accepted input forms, and returned content plus ambiguity handling. The size limit and assembly constraint are front-loaded where an agent will read them first.

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 single-query tool with a 100%-documented two-parameter schema, an output schema and full safety annotations, the description covers the remaining gaps an agent needs — variant scope limits and what happens on ambiguous input. Only minor omissions (e.g. what happens for out-of-scope variants) keep it from perfect.

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% and the query parameter already lists coordinates, HGVS, SPDI, rsID and the canonical key form, so the description largely restates the schema. It adds only the canonical key shape hint and the 'one variant' constraint, so baseline 3 applies.

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?

A precise verb (classify/interpret) plus a tightly bounded resource: one GRCh38 germline nuclear SNV or simple indel under 50 bp, under ACMG/AMP. The sibling tools are all gene- or literature-oriented, so this is trivially distinguishable as the variant-interpretation entry point.

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 establishes clear scope conditions — single variant, nuclear, GRCh38, under 50 bp — which implicitly exclude CNVs, structural variants and batch input, and it explains that ambiguous input yields candidates for selection. It stops short of naming alternatives or stating prerequisites, but for this tool family there is no real competing sibling.

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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