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musharna

plant-genomics-mcp

by musharna

Batch: BAR Gene Summary

batch_bar_gene_summary
Read-onlyIdempotent

Batch retrieve curator summaries, computational descriptions, NCBI Gene IDs, and cross-DB aliases for up to 50 Arabidopsis loci via parallel BAR ThaleMine and GAIA queries.

Instructions

Batch variant of bar_gene_summary. Fans out per-locus BAR ThaleMine + GAIA-aliases calls in parallel (up to 50 loci). Each results[locus] is the full single-locus payload (curator summary, computational description, NCBI Gene ID, cross-DB aliases). Arabidopsis only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lociYesList of locus identifiers (1–50). Successes land in results[locus]; PlantGenomicsError failures in errors[locus].

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)
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds behavioral context: fan-out in parallel, up to 50 loci, result structure, and organism restriction. No contradiction with 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?

Three sentences, front-loaded with purpose and constraints. No extraneous information. Every sentence adds value (batch variant, parallel fan-out, payload summary, organism restriction).

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 that an output schema exists (as per context), the description sufficiently explains return values without needing full schema details. Covers key payload fields and error handling, and includes scope (Arabidopsis) and limits (50 loci).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a description for the loci parameter. Description adds meaning about array items and error handling ('Successes land in results[locus]; PlantGenomicsError failures in errors[locus]'), which goes beyond the schema-provided description.

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?

Clearly states it's a batch variant of bar_gene_summary, describes parallel fan-out over up to 50 loci, and specifies the output structure (curator summary, computational description, NCBI Gene ID, cross-DB aliases). Distinguishes from sibling bar_gene_summary by being batch and from other batch tools by specifying the exact per-locus payload.

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 says 'Batch variant of bar_gene_summary' and 'Arabidopsis only', implying use for multiple loci. Does not explicitly state when not to use (e.g., for single locus), but the context is clear. Provides information about parallel execution and payload structure.

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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