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Glama

Ensembl

by io.github.pipeworx-io

Variation

variation
Read-onlyIdempotent

"What is [rsID]" / "look up SNP [rs...]" / "variant info for [rsN]" — fetch a genetic variation record by ID (e.g. rs56116432). Returns alleles, genomic location, clinical significance, gene mappings. Use for SNP lookups, pharmacogenomics, GWAS follow-up.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
speciesYes
variant_idYese.g. "rs56116432"

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoEnsembl variation ID
endNoEnd coordinate
nameNoVariant name/ID
classNoVariant class (SNP, indel, etc)
startNoStart coordinate
strandNoStrand
allelesNoAlleles
seq_region_nameNoChromosome
ancestral_alleleNoAncestral allele

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "species": "human",
+    "variant_id": "rs56116432"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "Variation record by variant ID",
+  "properties": {
+    "alleles": {
+      "description": "Alleles",
+      "items": {
+        "properties": {
+          "allele": {
+            "description": "Allele sequence",
+            "type": "string"
+          },
+          "frequency": {
+            "description": "Allele frequency",
+            "type": "number"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "ancestral_allele": {
+      "description": "Ancestral allele",
+      "type": "string"
+    },
+    "class": {
+      "description": "Variant class (SNP, indel, etc)",
+      "type": "string"
+    },
+    "end": {
+      "description": "End coordinate",
+      "type": "integer"
+    },
+    "id": {
+      "description": "Ensembl variation ID",
+      "type": "string"
+    },
+    "name": {
+      "description": "Variant name/ID",
+      "type": "string"
+    },
+    "seq_region_name": {
+      "description": "Chromosome",
+      "type": "string"
+    },
+    "start": {
+      "description": "Start coordinate",
+      "type": "integer"
+    },
+    "strand": {
+      "description": "Strand",
+      "type": "integer"
+    }
+  },
+  "type": "object"
+}
  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 readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so safety is covered. The description adds value by revealing the output content (alleles, location, clinical significance, gene mappings), which helps the agent set expectations but stops short of deeper caveats like data availability or versioning.

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 sentences, front-loaded with trigger phrases and examples. Every sentence adds meaning: first defines purpose, second lists returns and use cases. No redundancy or filler.

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?

Given the tool's simplicity (2 required params, rich annotations, output schema present), the description is complete. It covers purpose, usage scenarios, and output contents, and it leverages annotations and output schema for the rest. No critical information is 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 coverage is 50%: variant_id has a description (e.g., 'rs56116432') and species has none. The description reinforces variant_id semantics through examples but does not explain species values or required format. The schema example shows 'human' but the description itself adds little beyond that. This partially compensates but leaves a gap for the required species parameter.

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?

The description clearly states the tool fetches a genetic variation record by ID, with specific examples like 'What is [rsID]' and 'look up SNP [rs...]'. It also lists return fields (alleles, genomic location, clinical significance, gene mappings), distinguishing it from sibling tools like sequence or homology. The verb-resource pair is specific and unambiguous.

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 explicitly says 'Use for SNP lookups, pharmacogenomics, GWAS follow-up', providing clear context for when to invoke this tool. It does not mention alternatives or exclusions, so it falls short of a 5, but the guidance is sufficient for typical use.

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

A3.6/5.0
Disambiguation2/5

Multiple tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical), ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, while polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all analyze prediction markets. An agent would struggle to pick the correct tool without reading very long descriptions.

Naming Consistency3/5

All names are snake_case and readable, but the style is inconsistent: some are bare nouns (sequence, variation, homology), some are single verbs (lookup, recall, forget), and others are long descriptive phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). There is no consistent verb_noun or resource_noun pattern across the set.

Tool Count2/5

38 tools is excessive for a coherent server, and nearly all of them are unrelated to the server's stated name ('Ensembl') — only about 7 tools (lookup, lookup_symbol, sequence, variation, vep, xrefs, homology) actually belong to the Ensembl domain. The rest form several unrelated clusters (Pipeworx data queries, prediction markets, memory, subscriptions), making the tool count feel bloated and unfocused.

Completeness2/5

For an Ensembl server, the surface is thin: it covers ID lookup, sequence retrieval, variants, VEP, xrefs, and homology, but omits other core Ensembl functionality such as gene trees, alignments, regulation, expression, and assembly data. Meanwhile the many non-Ensembl tools don't form a complete domain of their own — they are a grab bag of unrelated utilities.

Resources