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Glama

Myvariant

by io.github.pipeworx-io

Variant

variant
Read-onlyIdempotent

Get the full merged annotation for a single human genetic variant by its HGVS id (e.g. "chr7:g.140453136A>T"). Returns annotations aggregated from dbSNP, ClinVar (pathogenicity / clinical significance), CADD and dbNSFP (deleteriousness/conservation scores), and gnomAD (population allele frequencies). Use to look up a known variant and read its pathogenicity and population frequency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesAn HGVS variant id, e.g. "chr7:g.140453136A>T".
fieldsNoComma-separated return fields (default: all). e.g. "clinvar,gnomad_genome.af,cadd.phred".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "id": "chr7:g.140453136A>T"
+  },
+  {
+    "fields": "clinvar,gnomad_genome.af,cadd.phred",
+    "id": "chr1:g.218631822G>A"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. Description adds value by listing aggregated data sources (dbSNP, ClinVar, CADD, dbNSFP, gnomAD) and what the return includes (pathogenicity, population frequency).

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?

Two concise sentences: first states purpose and input format, second states use case. No wasted words; front-loaded with key information.

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?

For a two-parameter lookup tool with rich annotations, the description covers input format, output nature (aggregated data), and use case. No gaps given the complexity.

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 coverage is 100% with descriptions for both parameters. Description provides examples and context for the fields parameter, but does not add substantial meaning beyond the schema.

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?

Description clearly states 'Get the full merged annotation for a single human genetic variant by its HGVS id', specifying the verb, resource, and input format. Differentiates from sibling tools which are unrelated.

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?

Explicitly states 'Use to look up a known variant and read its pathogenicity and population frequency', giving clear context. No explicit when-not-to-use, but sibling context makes differentiation obvious.

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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TDQS

A3.9/5.0
Disambiguation2/5

Several clusters have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the five prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_kalshi_spread, polymarket_fill_risk) all target 'find edge in Polymarket markets' with subtle differences. query and variant both retrieve the same variant annotations, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Most tools follow a consistent verb_noun or domain-prefixed pattern (ask_pipeworx, compare_entities, resolve_entity, polymarket_edges, remember/recall/forget). Minor deviations exist: the bare nouns query, variant, and metadata are less descriptive, and ask_pipeworx_beta uses a suffix instead of a clean verb pattern, but the overall convention is fairly uniform.

Tool Count2/5

34 tools is far too many for a server named 'Myvariant' whose stated domain is genetic variant annotations. The set is a grab-bag spanning genetic data, Pipeworx query routing, Polymarket betting, memory persistence, subscriptions, AI visibility, and npm dependency scanning. Most tools are unrelated to the server's apparent purpose, making the count feel bloated and incoherent.

Completeness3/5

Individual clusters are reasonably complete: variants have search/get/metadata, memory has remember/recall/forget, and subscriptions have subscribe/list/unsubscribe/alerts. However, as a Myvariant server the surface is massively over-scoped yet oddly missing any batch-variant or annotation-source-specific lookup, and the sprawling multi-domain design makes 'complete' hard to meaningfully assess.

Resources