STRING-MCP

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 only states the basic function without mentioning any behavioral traits such as rate limits, authentication needs, error handling, or what happens if mapping fails. For a tool with no annotation coverage, this is a significant gap in transparency.

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 extremely concise and front-loaded with a single, clear sentence: 'Map protein identifiers to STRING IDs.' There is no wasted text, and it efficiently communicates the core purpose without unnecessary details, making it easy for an AI agent to parse quickly.

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 complexity of mapping identifiers (which could involve errors or partial matches), no annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like, how results are structured, or any edge cases. This lack of context makes it inadequate for a tool that likely returns structured data, leaving the agent with insufficient information to use it effectively.

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 no parameter semantics beyond what the input schema provides. Since schema description coverage is 100%, the schema already documents all parameters well (e.g., 'identifiers' as 'Protein list', 'species' as 'NCBI/STRING taxon'). The baseline score of 3 is appropriate as the schema does the heavy lifting, but the description doesn't enhance understanding of parameter usage or interactions.

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: 'Map protein identifiers to STRING IDs.' It specifies the action ('Map') and the resource ('protein identifiers'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_homology' or 'get_interaction_partners', which might also involve protein identifiers but serve different purposes.

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, context, or exclusions, nor does it refer to sibling tools. This lack of usage context makes it harder for an AI agent to decide when this tool is appropriate compared to others in the server.

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