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plant-genomics-mcp

by musharna
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Python
Remote

Batch: Gramene Homologs

batch_gramene_homologs
Read-onlyIdempotent

Retrieve homologs for up to 50 plant gene loci per call, filtering by ortholog/paralog type or target organism. Returns a structured batch envelope with counts, results, and errors.

Instructions

Batch version of gramene_homologs. Up to 50 loci per call; shares the homology_type filter across all loci. Returns the standard batch envelope (count + results dict + errors dict).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lociYesList of locus identifiers (max 50)
homology_typeNoortholog
with_organismNoAdd 'organism' (Gramene species slug, null when unknown) to every row without filtering; one extra call per 100 rows (#130)
target_organismNoKeep only homologs in this organism (slug, scientific/common name, or NCBI taxid), filtered BEFORE the cap so a hub gene's rice or wheat orthologs cannot be pushed past 'limit' by other species. Adds 'organism' to every row and 'total_all_organisms'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYesThe batch tool name, e.g. batch_resolve_locus_to_uniprot
countYesNumber of distinct loci queried, returned (== len(results) + len(errors)). The input list is de-duplicated first, so this is LOWER than the number of loci you sent if you sent a duplicate — that is de-duplication, not a dropped locus.
errorsYeslocus → '[ClassName] message' for PlantGenomicsError failures
resultsYeslocus → per-locus result dict (same shape as the single-locus tool)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.22.0
    • addedInput schema / properties / loci / minItems
      Added value: +1
    • addedInput schema / properties / target_organism
      Added value: +{
+  "description": "Keep only homologs in this organism (slug, scientific/common name, or NCBI taxid), filtered BEFORE the cap so a hub gene's rice or wheat orthologs cannot be pushed past 'limit' by other species. Adds 'organism' to every row and 'total_all_organisms'.",
+  "type": [
+    "string",
+    "integer"
+  ]
+}
    • addedInput schema / properties / with_organism
      Added value: +{
+  "default": false,
+  "description": "Add 'organism' (Gramene species slug, null when unknown) to every row without filtering; one extra call per 100 rows (#130)",
+  "type": "boolean"
+}
  2. Changed1 schema field changedv1.19.4
    • changedOutput schema / properties / count / description
      Previous value: -"Number of loci in the input list"New value: +"Number of distinct loci queried, returned (== len(results) + len(errors)). The input list is de-duplicated first, so this is LOWER than the number of loci you sent if you sent a duplicate — that is de-duplication, not a dropped locus."
  3. First observedv1.8.0

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover read-only and idempotent safety. The description adds the 50-locus limit, the shared filter behavior, and the return envelope structure (count + results + errors), which are useful behavioral details beyond the schema and annotations.

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 with zero waste. It front-loads the batch nature and key constraints (cap, filter sharing, return format) and avoids unnecessary elaboration.

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 detailed parameter descriptions, the description covers the essential cap and filter behavior. It doesn't mention optional parameters like with_organism or target_organism, but those are fully described in the schema, so an agent has enough to call it correctly.

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 75% (homology_type lacks a description). The description adds that homology_type is shared across all loci, giving meaning to that parameter. Other parameters are well-documented in the schema, so the description adds value where needed without redundancy.

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 clearly states it is a batch version of gramene_homologs, with a 50-locus cap and shared homology_type filter. This distinguishes it from the single-locus sibling and conveys the core function without ambiguity.

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 'Batch version' phrasing implies use for multiple loci, and the 50-locus cap hints at splitting larger requests. However, it doesn't explicitly name the single-locus alternative or state when not to use it, nor does it differentiate from other batch tools like batch_locus_call.

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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