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uniprot_get_evidence_summary

Read-only

Summarizes ECO evidence codes for a UniProt entry, computing a confidence score (0-100) based on experimental, manual, and automatic annotations to distinguish wet-lab confirmed from computationally inferred evidence.

Instructions

Summarise and grade the ECO (Evidence and Conclusion Ontology) codes attached to a UniProt entry's annotations. Counts how many features and comments cite each evidence code, then classifies every occurrence as experimental (wet-lab, ECO:0000269), manual (curator-reviewed inference), or automatic (un-reviewed pipeline call) and collapses that into a single 0-100 evidence-confidence score with a high / moderate / low / very-low band. A score near 100 means the entry is dominated by direct experimental evidence; a score near 10 means it is almost entirely computationally inferred. Critical for any downstream agent that must distinguish 'wet-lab confirmed' annotations from 'inferred by similarity'. JSON output adds an evidence_confidence block (score, band, per-class breakdown, weights) alongside the raw evidence_counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
accessionYesUniProt accession, e.g. 'P04637' (human TP53) or 'P38398' (human BRCA1). Both reviewed (Swiss-Prot) and unreviewed (TrEMBL) accessions are accepted; case-sensitive.
response_formatNo'markdown' (default) for a human-readable report with a provenance footer, or 'json' for a machine-parseable structured payload with the same data. Any other value is rejected.markdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

The description goes beyond annotations (readOnlyHint, openWorldHint) by detailing the scoring algorithm, classification scheme, and output structure. It explains what a score of 100 vs. 10 means, providing actionable behavioral insights for downstream agents.

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 (4 sentences) and front-loaded with the core purpose. It efficiently covers why, how, and the output format without extraneous words. Every sentence contributes meaningful information.

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 presence of an output schema, the description adequately explains the tool's behavior, scoring rationale, and output blocks (evidence_confidence, evidence_counts). It provides sufficient context for an agent to select and invoke the tool 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 coverage is 100%, so the baseline is 3. The description adds value by providing concrete examples (accession like 'P04637'), clarifying case-sensitivity for accessions, and elaborating on response_format options (markdown with provenance footer vs. JSON). These details enhance understanding beyond the 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?

The description clearly states the tool summarizes and grades ECO codes for a UniProt entry, counts evidence code usage, classifies into experimental/manual/automatic, and outputs a 0-100 confidence score. This specific verb-resource combination distinguishes it from sibling tools like uniprot_get_features or uniprot_get_entry.

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?

The description explicitly calls out the critical use case: distinguishing 'wet-lab confirmed' from 'inferred by similarity'. This provides clear context for when to use the tool, though it does not explicitly mention when not to use it or name specific alternatives.

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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