BioPython MCP Server

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build_phylogenetic_tree

Build a phylogenetic tree from aligned sequences using neighbor-joining or UPGMA methods. Specify sequences, choose method, and add optional labels to infer evolutionary relationships.

Instructions

Build a phylogenetic tree from sequences.

Args: sequences: List of aligned sequences method: Tree building method - 'nj' (neighbor-joining) or 'upgma' (default: 'nj') labels: Optional labels for sequences

Returns: Dictionary containing tree information

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": "build_phylogenetic_tree"
  }
}

Input Schema

Name	Required	Default
`sequences`	Yes
`method`	No	nj
`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

A3.6/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits beyond what the schema implies, such as computation limits, required input constraints (e.g., aligned sequences), or error conditions. The return type is vaguely described without additional context.

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 concise, starting with the purpose and then listing parameters and return type. However, the args section is somewhat verbose and could be condensed without losing clarity.

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?

Given the tool's moderate complexity and the existence of an output schema, the description provides adequate but minimal context. It does not discuss input validation or use cases, leaving gaps in comprehensiveness.

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

Parameters4/5

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

With 0% schema description coverage, the description adds significant meaning: it explains the sequences parameter explicitly, details the method options and default, and clarifies that labels are optional. This compensates well for the lack of schema descriptions.

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's action ('Build a phylogenetic tree') and identifies the key resource ('from sequences'). It distinguishes itself from siblings like calculate_distance_matrix and draw_tree by specifying the output is a tree structure.

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?

The description does not explicitly state when to use this tool versus alternatives, such as when sequences are already aligned versus needing alignment first. The implication is present but not overt, and no exclusions or prerequisites are mentioned.

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