Skip to main content
Glama
musharna

plant-genomics-mcp

batch_locus_go_annotations

Retrieve Gene Ontology annotations for up to 50 plant loci by resolving each to UniProt and querying QuickGO. Handles errors per locus, returning results and failures separately.

Instructions

Batch variant of locus_go_annotations. Two-stage fanout — each locus is resolved to UniProt and then queried in QuickGO. Per-locus NotFoundError from either stage lands in errors[] with the typed prefix preserved. Capped at 50 loci.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lociYesList of locus identifiers (1–50). Successes land in results[locus]; PlantGenomicsError failures in errors[locus].
organismNoPlant organism — accepts canonical slug (arabidopsis_thaliana), scientific or common name, or NCBI taxidarabidopsis_thaliana
limitNoMax annotations per locus from QuickGO (1–100, default 50)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYesThe batch tool name, e.g. batch_resolve_locus_to_uniprot
countYesNumber of loci in the input list
resultsYeslocus → per-locus result dict (same shape as the single-locus tool)
errorsYeslocus → '[ClassName] message' for PlantGenomicsError failures
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the two-stage process, error handling (NotFoundError per locus in errors[]), and a cap of 50 loci. It does not mention side effects or permissions, but for a read-only query tool, this is reasonably transparent.

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?

The description is extremely concise at three sentences, with the key information front-loaded. Every sentence adds value: batch variant, two-stage process, error handling, and limit. No wasted words.

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 tool's complexity (batch, two-stage, external queries) and the presence of an output schema (not shown), the description covers the main points: process, error handling, and cap. It could be slightly more complete by mentioning the expected return structure or referring to the single variant, but it is sufficient.

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%, so the baseline is 3. The description adds context about the two-stage resolution and error handling, but the parameter descriptions in the schema already explain the loci array, organism, and limit adequately. The description does not significantly enhance parameter understanding 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?

The description clearly states it is a batch variant of locus_go_annotations, specifying the two-stage fanout process (UniProt then QuickGO) and per-locus error handling. This distinguishes it from its single-locus sibling and clarifies its function.

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 identifies the tool as a batch variant, implying use for multiple loci rather than a single one. While it does not explicitly list when not to use it or suggest alternatives, the context is clear given the sibling tools include the single-locus version.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/musharna/plant-genomics-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server