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musharna

plant-genomics-mcp

by musharna

PDBe: Experimental Structures

experimental_structures
Read-onlyIdempotent

Retrieve experimentally-solved protein structures for a plant locus using PDBe. Returns ranked PDB entries with experimental method, resolution, and coverage; indicates when no structure exists.

Instructions

Fetch experimentally-solved (X-ray / cryo-EM / NMR) protein structures for a locus from PDBe (www.ebi.ac.uk/pdbe; free, no key). Resolves the locus → UniProt accession, then returns PDBe's best_structures mapping ranked best-first: per entry the PDB id, chain, experimental method, resolution, coverage, and modelled residue span. Most plant proteins have NO deposited structure — that returns found=false (a normal outcome, not an error); a locus with no UniProt entry raises a typed NotFoundError. structure_count is the true total even when the list is capped. Complements alphafold_structure (the predicted view). Works for all 12 organisms (UniProt-keyed). Defaults to arabidopsis_thaliana; pass organism= for other species.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
locusYese.g. AT4G09760 (Arabidopsis), Os01g0100100 (rice RAP-DB)
organismNoPlant organism — accepts canonical slug (arabidopsis_thaliana), scientific or common name, or NCBI taxidarabidopsis_thaliana

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
foundYesTrue if any experimental structure is deposited
locusYes
accessionYesResolved UniProt accession
truncatedYesTrue if the structure list was capped
structuresNoBest-first {pdb_id, chain_id, experimental_method, resolution, coverage, …}
structure_countYesTotal deposited structures (pre-cap)
Behavior5/5

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

Discloses resolution process, return format, error behavior (NotFoundError), and that structure_count is true total. Consistent with readOnly/idiempotent annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured, front-loaded, each sentence adds value. Slightly verbose but still concise.

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?

Covers error cases, normal outcomes, data fields, complements siblings. Output schema and annotations further reduce burden.

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?

Adds context beyond schema: explains locus resolution, organism defaults/accepts multiple forms, and example values. Schema coverage was already 100%.

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 fetches experimentally-solved structures for a locus, specifies methods (X-ray, cryo-EM, NMR), and differentiates from alphafold_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 context about when to use (experimental vs predicted) and normal outcomes (found=false for most plants). Could explicitly state when not to use.

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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