BioPython MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool calculates GC content but doesn't mention any behavioral traits such as input validation (e.g., handling invalid characters), performance characteristics, error handling, or side effects. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections are structured but slightly redundant since an output schema exists. Every sentence adds value, though it could be more concise by omitting the return statement given the output schema.

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

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

Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is mostly complete. It covers the purpose and parameter semantics adequately. However, it lacks usage guidelines and behavioral details, which are minor gaps in this simple context.

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

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

The description adds meaningful context for the single parameter 'sequence', specifying it as a 'DNA or RNA sequence string'. Since schema description coverage is 0% (the schema only indicates it's a string), this compensates well by clarifying the expected content. However, it doesn't detail format constraints (e.g., case sensitivity, allowed characters), which prevents a perfect score.

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

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

The description clearly states the tool's purpose: 'Calculate the GC content of a DNA or RNA sequence.' It specifies the verb ('calculate') and resource ('GC content'), and distinguishes it from siblings like 'calculate_alignment_score' or 'calculate_distance_matrix' by focusing on nucleic acid composition analysis. However, it doesn't explicitly contrast with all sibling tools (e.g., 'transcribe_dna' or 'translate_sequence'), which is why it's a 4 rather than a 5.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, constraints, or comparative context with sibling tools like 'calculate_structure_stats' or 'find_motif'. The agent must infer usage solely from the purpose statement, which is insufficient for optimal tool selection.

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