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get_uniprot_summary

Retrieve AlphaFold structure model summaries for a UniProt accession, entry name, or CRC64 checksum, including model URLs, confidence scores, and coverage.

Instructions

This tool fetches AlphaFold model predictions for a given UniProt residue range. The tool takes in a UniProt accession number (AC), entry name (ID), or CRC64 checksum and returns detailed information about the structure models available for the specified sequence range.

Arguments

  • qualifier (str): UniProtKB accession number (AC), entry name (ID), or CRC64 checksum of the UniProt sequence (e.g., 'Q5VSL9').

Example Input:

  • qualifier: Q5VSL9

Response Structure

The response includes detailed information about the UniProt entry and its associated AlphaFold models.

Example Response:

{
  "uniprot_entry": {
    "ac": "Q5VSL9",
    "id": "STRP1_HUMAN",
    "uniprot_checksum": "5F9BA1D4C7DE6925",
    "sequence_length": 837,
    "segment_start": 1,
    "segment_end": 837
  },
  "structures": [
    {
      "summary": {
        "model_identifier": "AF-Q5VSL9-F1",
        "model_category": "AB-INITIO",
        "model_url": "https://alphafold.ebi.ac.uk/files/AF-Q5VSL9-F1-model_v4.cif",
        "model_format": "MMCIF",
        "model_type": null,
        "model_page_url": "https://alphafold.ebi.ac.uk/entry/Q5VSL9",
        "provider": "AlphaFold DB",
        "number_of_conformers": null,
        "ensemble_sample_url": null,
        "ensemble_sample_format": null,
        "created": "2022-06-01",
        "sequence_identity": 1,
        "uniprot_start": 1,
        "uniprot_end": 837,
        "coverage": 1,
        "experimental_method": null,
        "resolution": null,
        "confidence_type": "pLDDT",
        "confidence_version": null,
        "confidence_avg_local_score": 80.82,
        "oligomeric_state": null,
        "preferred_assembly_id": null,
        "entities": [
          {
            "entity_type": "POLYMER",
            "entity_poly_type": "POLYPEPTIDE(L)",
            "identifier": "Q5VSL9",
            "identifier_category": "UNIPROT",
            "description": "Striatin-interacting protein 1",
            "chain_ids": [
              "A"
            ]
          }
        ]
      }
    }
  ]
}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qualifierYes
output_jsonNo
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It provides a detailed response example and explains the accepted qualifier formats, which is useful. However, the inaccurate reference to a 'residue range' parameter and the lack of any mention of error behavior or limitations reduce transparency.

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?

The description is well-structured with clear sections for arguments and response structure. It is front-loaded with the main purpose. The extensive JSON example is lengthy but directly illustrates the output, earning its place. Minor redundancy exists, but the overall organization is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema, the response example provides strong completeness for return values. However, the description does not address when to use this tool relative to siblings, and the misleading residue-range statement creates confusion. These gaps prevent a higher score.

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?

The description adds significant meaning to the qualifier parameter by specifying it can be a UniProt AC, ID, or CRC64 checksum, with a concrete example. This goes well beyond the schema's generic string type. The output_json parameter is not described, but its name and default value are self-explanatory.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear action ('fetches AlphaFold model predictions'), but the tool name is get_uniprot_summary and the response example includes UniProt entry details, creating confusion about the actual purpose. It also references a 'residue range' argument that does not exist in the schema. The description does not distinguish this tool from the sibling get_alphafold_prediction.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like get_alphafold_prediction or get_annotations. The description simply explains what the tool does and gives an example input, without any context about selection criteria or exclusions.

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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