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uniprot_get_alphafold_confidence

Read-only

Fetch model-level AlphaFold DB confidence metadata for an accession.

Instructions

Fetch model-level AlphaFold DB confidence metadata for an accession.

Returns mean pLDDT and the four-band distribution of local model confidence, not individual residue scores. Checks the exact UniProt accession so an isoform's prediction cannot be substituted for the canonical protein. For multiple same-accession models or fragments, JSON output includes additionalModelSummaries with separate ranges. Global pLDDT does not prove experimental accuracy or validate confidence at a particular residue or between domains. Call uniprot_resolve_alphafold for the UniProt cross-reference.

This tool calls https://alphafold.ebi.ac.uk — declared in PRIVACY.md as a third party. Provenance carries source = AlphaFoldDB.

Input 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

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.3.0
    • addedInput schema / properties / accession / description
      Added value: +"UniProt accession, e.g. 'P04637' (human TP53) or 'P38398' (human BRCA1). Both reviewed (Swiss-Prot) and unreviewed (TrEMBL) accessions are accepted; case-sensitive."
    • addedInput schema / properties / response_format / description
      Added value: +"'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."
  2. First observedv1.1.9

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only cover readOnly and openWorld, so the description carries real weight: it discloses that this calls a third-party endpoint (alphafold.ebi.ac.uk per PRIVACY.md), names the provenance source (AlphaFoldDB), explains the exact-accession matching behavior preventing isoform substitution, and notes additionalModelSummaries for multiple/fragment models. This is far beyond what the annotations provide.

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?

Front-loaded with the core action, then layered with useful caveats. It is dense and every sentence carries information (scope, caveats, sibling pointer, third-party disclosure), though it runs long and could consolidate the closing provenance/privacy lines.

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?

With an output schema present the return details need not be repeated, yet the description still clarifies interpretation limits ('does not prove experimental accuracy'). Combined with the third-party and provenance disclosures, an agent has everything needed to call and interpret it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (accession, response_format) are already fully documented in the schema. The description reinforces the response-format distinction by noting what JSON output contains, but adds no syntax or format detail beyond what the schema states — baseline 3 applies.

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?

States a specific verb ('Fetch') and resource ('model-level AlphaFold DB confidence metadata for an accession'), and immediately scopes what is returned — mean pLDDT plus the four-band distribution, not per-residue scores. This lets an agent distinguish it from siblings like uniprot_resolve_alphafold without opening a schema.

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?

Explicitly routes the agent to a sibling ('Call uniprot_resolve_alphafold for the UniProt cross-reference') and states the boundary of what this tool does NOT return (individual residue scores, residue-level or inter-domain validation). It gives a clear positive/negative scope but does not enumerate a fuller set of alternatives.

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