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BioPython MCP Server

BioPython MCP Server

by kmaneesh
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

translate_sequence

Translate DNA or RNA sequences into protein using customizable genetic code tables. Optionally stop at first stop codon for accurate peptide prediction.

Instructions

Translate a DNA or RNA sequence to protein.

Args: sequence: DNA or RNA sequence string table: Genetic code table to use (default: 1 for standard code) to_stop: Stop translation at first stop codon (default: False)

Returns: Dictionary containing the translated protein sequence and metadata

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": {
      "sequence": "<sequence>"
    },
    "name": "translate_sequence"
  }
}

Input Schema

NameRequiredDescriptionDefault
sequenceYes
tableNo
to_stopNo

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.8

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: translation uses a genetic code table, can stop at first stop codon, and returns a dict with protein sequence and metadata. However, it does not mention error handling or input validation.

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 a clear purpose sentence followed by Args and Returns sections. It is slightly verbose but front-loaded and free of redundancy.

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 tool's moderate complexity (3 parameters) and the presence of an output schema, the description provides sufficient context: it covers all parameters, return value, and key behavior. No critical gaps remain.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully explains each parameter: sequence as DNA/RNA string, table as genetic code table with default 1, to_stop as stop at first stop codon. This adds significant meaning beyond the schema's type and defaults.

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 'Translate a DNA or RNA sequence to protein,' providing a specific verb and resource. It distinguishes itself from sibling tools like transcribe_dna and reverse_complement.

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 given on when to use this tool versus alternatives, nor are any prerequisites or exclusions mentioned. The description simply states what it does.

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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