protein-tools-mcp

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aa_composition

Compute protein molecular weight, amino acid composition, charge, GRAVY, and pI from FASTA files or raw sequences, while rejecting non-protein input with structured errors naming detected type.

Instructions

Protein physicochemistry: MW, aa composition, charge, GRAVY, pI estimate.

Parses a FASTA file or raw text (record sequence_index); rejects non-protein sequences with a structured error naming the detected type. Free; local compute.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "source": "<source>"
    },
    "name": "aa_composition"
  }
}

Input Schema

Name	Required	Description	Default
`source`	Yes
`is_file`	No
`sequence_index`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 20, 2026

TDQS

A3.5/5.0

Behavior4/5

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

With no annotations, the description carries the burden and discloses useful traits: parsing modes, error behavior (rejects non-protein with a structured error naming the detected type), and operational profile (free, local compute). It stops short of explicitly confirming read-only/no side effects, but local compute strongly implies it.

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?

Three short sentences, front-loaded with what the tool computes, then input/error behavior, then a terse operational note. Every phrase earns its place with no redundancy.

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?

An output schema exists, so return values need not be described. But with no annotations and 0% parameter description coverage, the unexplained is_file behavior leaves the definition incomplete for correct invocation, especially when passing raw text.

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

Parameters2/5

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

Schema description coverage is 0% across 3 parameters, so the description must compensate. It clarifies that source can be a FASTA file or raw text and mentions sequence_index for record selection, but never explains the is_file boolean (default true), which determines whether source is treated as a file path or raw sequence. That gap can cause incorrect invocation.

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

Purpose4/5

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

States a specific resource and output set (MW, aa composition, charge, GRAVY, pI) and the input modes (FASTA file or raw text). It does not explicitly name or distinguish a sibling such as fasta_stats or propka_summary, so it falls short of a 5.

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

Usage Guidelines3/5

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

Usage is implied by the protein-specific scope and the explicit exclusion of non-protein sequences, which are rejected with a structured error. However, the description gives no guidance on when to choose this tool over siblings like fasta_stats or propka_summary.

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