Skip to main content
Glama

uniprot_get_processing_features

Read-only

Retrieves signal peptides, propeptides, and other maturation features for a UniProt protein, showing how the polypeptide is cleaved into its mature form for therapeutic engineering and secretion analysis.

Instructions

Return the maturation and processing features (signal peptide, propeptide, transit peptide, initiator methionine, chain, peptide). These describe how the translated polypeptide is cleaved and targeted into its mature form — essential for therapeutic-protein engineering and pathogen-secretion-system analysis. A pre-filtered view over uniprot_get_features; for post-translational chemical modifications instead of cleavage/targeting, use uniprot_get_ptms.

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
Behavior3/5

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

Annotations already declare readOnlyHint and openWorldHint. Description adds context that it's a pre-filtered view but doesn't disclose additional behavioral traits. No contradiction.

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?

Two sentences, front-loaded with purpose and usage, no wasted words.

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?

Has output schema, so return values are covered. Description explains feature types, related tools, and use cases. Complete for this 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 coverage is 100%, so the schema already documents both parameters. Description does not add extra meaning beyond what's in 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 it returns maturation and processing features (signal peptide, propeptide, etc.) and distinguishes it from related tools like uniprot_get_ptms and uniprot_get_features.

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 tells when to use (for cleavage/targeting) and when not (for PTMs, use uniprot_get_ptms). Also provides use cases like therapeutic-protein engineering and pathogen-secretion-system analysis.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/smaniches/uniprot-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server