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Query Protein Research Database

query_protein_database

Read-onlyIdempotent

Retrieve previously stored proteins from the local knowledge graph by filtering on mean AlphaFold pLDDT and druggability tier.

Instructions

Recall proteins already stored in the local knowledge graph.

This is a local-recall query, not a live lookup: it returns only proteins that have previously been written to the local SQLite store (the curated boot seed, plus anything added through the knowledge-graph storage API). No upstream API is called. To assess a protein that may not be stored yet, use assess_target_druggability (druggability tier) or analyze_structural_confidence (pLDDT), which query live sources.

Filters are combined with AND; omit a filter to leave that dimension unconstrained. Returns a JSON record with the applied query, a result_count, and the matching proteins rows. The list is empty when nothing stored matches — common when only the boot seed is loaded, so a broad filter returning few rows usually means the store is small, not that no such protein exists.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv1.4.3Jul 30, 2026

changedInput schema / properties / params / properties / druggability_tier / description

Previous value: -"Druggability tier filter."New value: +"Keep only stored proteins with this druggability tier; omit for any tier."

addedInput schema / properties / params / properties / limit / description
```
Added value: +"Maximum rows to return (1–500)."
```

changedInput schema / properties / params / properties / min_plddt / description

Previous value: -"Minimum mean pLDDT confidence score."New value: +"Minimum stored mean AlphaFold pLDDT (0–100; ≥70 = high confidence)."

First observedv1.1.8May 29, 2026

TDQS

A4.9/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true. Description adds that no upstream API is called, describes underlying SQLite store, and explains that empty results are common when only boot seed is loaded, providing useful behavioral context beyond 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?

Description is well-structured with clear purpose first, then behavior, then usage notes. Every sentence adds value, no redundancy. Length is appropriate for the tool's complexity.

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 the tool has an output schema (implied), the description covers all necessary context: local-only nature, storage source, filter semantics, and interpretation of empty results. Completely adequate for an agent to use correctly.

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 already has descriptive parameter descriptions (e.g., min/max for limit, enum for druggability_tier). Description adds semantics about AND combination of filters and the meaning of omitting filters, which is not in schema. Slightly above baseline of 3 due to added filter logic insight.

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?

Description clearly states it recalls proteins from a local knowledge graph, uses specific verb 'recall' and resource 'proteins already stored'. Distinguishes from live-lookup siblings by naming alternatives and contrasting local vs live.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (local recall) and when not (for proteins not yet stored), provides alternatives (assess_target_druggability, analyze_structural_confidence), and explains filter combination behavior and interpretation of empty results.

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