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musharna

plant-genomics-mcp

batch_bar_aiv_interactions

Retrieve BAR AIV interactions for up to 50 loci in a single batch call. Returns gene regulatory network papers for Arabidopsis or protein-protein interaction partners for rice.

Instructions

Batch variant of bar_aiv_interactions. Fans out per-locus BAR AIV calls in parallel (up to 50 loci); all loci in a single call share the same organism. Each results[locus] is the full single-locus payload (kind=grn_papers for Arabidopsis with papers list, kind=ppi_predictions for rice with partners list).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lociYesList of locus identifiers (1–50). Successes land in results[locus]; PlantGenomicsError failures in errors[locus].
organismNoarabidopsis_thaliana or oryza_sativa — slug, scientific/common name, or NCBI taxidarabidopsis_thaliana

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?

No annotations provided, so description carries full burden. Discloses parallel fan-out, up to 50 loci, shared organism, per-locus result structure, and error handling. Does not mention rate limits or permissions, but adds significant behavioral detail beyond schema.

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 packed with essential information, front-loaded with purpose, no redundant words.

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 output schema exists, description provides complete behavioral overview: batch nature, limit, organism sharing, per-locus result keys. Covers all necessary aspects for an agent to use the tool effectively.

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 coverage is 100% with descriptions for both parameters. Description reinforces max 50 loci and shared organism, and adds context about parallel execution and per-locus result payload. Adds value beyond 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?

Clearly states 'Batch variant of bar_aiv_interactions' with specific verbs: fans out, parallel, up to 50 loci. Distinguishes from sibling bar_aiv_interactions by specifying batch behavior and per-locus payload structure.

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?

Provides clear usage context: batch multiple loci (1-50) with shared organism. Implicitly suggests use when needing parallel single-locus calls, but does not explicitly exclude other use cases or mention when to prefer the single-locus variant.

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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