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musharna

plant-genomics-mcp

batch_resolve_locus_to_uniprot

Resolves up to 50 plant locus identifiers to UniProtKB records in a single batch, providing accession IDs, gene names, organism details, and sequence length.

Instructions

Batch variant of resolve_locus_to_uniprot. Fans out per-locus UniProtKB searches in parallel (up to 50 loci). Each results[locus] is the full single-locus record (primaryAccession + uniProtkbId + entryType + geneNames + organism + sequenceLength + web_url + …).

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

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 provided, the description fully carries the burden. It discloses parallel execution, a limit of 50 loci, and the result structure (successes in results[locus], failures in errors[locus]). It also hints at the single-locus record contents, offering transparency beyond basic parameter info.

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 concise at two sentences, with the first sentence immediately stating the batch nature and parallelism. Every sentence provides essential information without redundancy or filler.

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 (parallel batch, error handling, and rich output), the description covers key aspects: parallel fan-out, item limit, result vs error separation, and a preview of result record fields. The presence of an output schema reduces the need to detail return values, but the description is sufficient for an agent to understand behavior.

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%, so baseline is 3. The description adds value by explaining that loci are processed in parallel and that errors are captured separately. It also clarifies the default for organism and the structure of results, going beyond the schema's minimal descriptions.

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 this is a batch variant of resolve_locus_to_uniprot, performing parallel per-locus searches for up to 50 loci. It specifies the verb (batch resolve), resource (UniProt KB), and scope (up to 50 loci), distinctly separating it from the single-locus sibling tool.

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 description implies use when you have multiple loci by stating 'batch variant' and 'fans out per-locus searches', but it does not explicitly state when to use this versus alternatives (e.g., single-locus version or other batch tools). No exclusion criteria or when-not guidance is provided.

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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