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uniprot_get_ptms

Read-only

Retrieve post-translational modifications (PTMs) including phosphorylation, glycosylation, and disulfide bonds for a given UniProt accession. Filtered from all features, with pointers to mass-spec databases for further evidence.

Instructions

Return the post-translational modification features (modified residues, glycosylation sites, lipidation sites, disulfide bonds, cross-links). PTMs are functionally critical: they switch enzymes on, target proteins for degradation, anchor them to membranes, and fold them via disulfides. A pre-filtered view over uniprot_get_features; for cleavage/targeting features instead of chemical modifications, use uniprot_get_processing_features. The empty case carries an honest pointer to mass-spec databases (PhosphoSitePlus, GlyConnect) for additional evidence.

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 adds valuable behavioral context beyond annotations (readOnlyHint, openWorldHint), such as explaining the functional importance of PTMs and acknowledging that empty results are an honest case with pointers to external databases. No contradiction with 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?

The description is well-structured and front-loaded with the list of PTM types. While every sentence adds value, it could be slightly more concise. Overall, it is efficient and clear.

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 does not need to detail return values. It fully covers the tool's purpose, usage context, sibling differentiation, and edge cases (empty results), making it highly complete for a lookup tool.

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%, with both parameters already fully described in the input schema. The tool description does not add new parameter information, but the provided schema descriptions are clear and complete, so a baseline score of 3 is appropriate.

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 returns post-translational modification features (modified residues, glycosylation sites, etc.) and explicitly distinguishes from the sibling tool uniprot_get_processing_features for cleavage/targeting features. It uses specific verbs and resource types.

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?

The description provides explicit guidance on when to use this tool versus alternatives, including directing users to uniprot_get_processing_features for different feature types. It also advises on empty results by pointing to mass-spec databases like PhosphoSitePlus and GlyConnect.

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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