CrowdCent 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 mentions the return type ('Dictionary containing list of training datasets'), which adds some value, but lacks details on permissions, pagination, error handling, or data format. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 brief and front-loaded with the main purpose, followed by a return statement. Both sentences are relevant, with no wasted words. However, the structure could be slightly improved by integrating the return info more seamlessly, but it's still highly efficient.

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 simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose and return type, which is sufficient for a straightforward list operation. However, it lacks details on behavioral aspects like error cases or data structure, leaving room for improvement in completeness.

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 tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info beyond the schema, but with no parameters, a baseline of 4 is appropriate as there's nothing to compensate for or improve upon.

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 verb ('List') and resource ('training datasets') with scope ('for the current challenge'), making the purpose specific and understandable. It distinguishes from siblings like 'get_training_dataset_info' (detailed info) and 'download_training_dataset' (download). However, it doesn't explicitly differentiate from 'list_all_challenges' (different resource), so it's not a perfect 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 minimal guidance, stating only 'for the current challenge' without explaining when to use this tool versus alternatives like 'get_training_dataset_info' or 'download_training_dataset'. No explicit when/when-not instructions or prerequisites are given, leaving usage context implied rather than clearly defined.

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