BioPython MCP Server

Overview Schema Source Related Servers Discussions Admin

calculate_distance_matrix

Compute pairwise distance matrices from aligned sequences. Specify a distance model (default 'identity') and optional labels to quantify sequence similarity. Returns a dictionary containing the distance matrix.

Instructions

Calculate pairwise distance matrix for sequences.

Args: sequences: List of aligned sequences model: Distance model to use (default: 'identity') labels: Optional labels for sequences

Returns: Dictionary containing distance matrix

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": {
      "sequences": "<sequences>"
    },
    "name": "calculate_distance_matrix"
  }
}

Input Schema

Name	Required	Default
`sequences`	Yes
`model`	No	identity
`labels`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.8Feb 5, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description must carry behavioral disclosure but only mentions parameters and returns. It does not explain possible models, error conditions, or performance characteristics, leaving the agent with minimal insight beyond the schema.

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 relatively concise with no redundant text, but the structure mixes a brief purpose with parameter list in a docstring format, which is functional yet not maximally efficient.

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

Completeness2/5

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

Given the tool's complexity, no output schema details, and sibling tools requiring differentiation, the description is incomplete; it omits expected input formats, model options, and example usage, leaving significant gaps for an agent.

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 0%, so the description adds meaning by naming param roles (aligned sequences, model with default, optional labels). However, it lacks details on model options, format constraints, or label usage, only partially compensating for the coverage gap.

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?

The description clearly states the tool calculates a pairwise distance matrix for sequences, specifying the resource and action. It distinguishes from siblings like build_phylogenetic_tree by focusing on distances, but could be more explicit about the requirement for aligned sequences.

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 on when to use this tool versus alternatives. While it implies usage for computing distances from aligned sequences, it does not mention exclusions or compare to siblings like pairwise_align or calculate_alignment_score.

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