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 states the tool submits predictions, implying a write operation, but doesn't disclose critical traits like required permissions, whether submissions are reversible, rate limits, or error handling. The return value is vaguely described as a 'Dictionary with submission details' without specifying content or structure, leaving gaps in understanding the tool's behavior.

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, starting with the core purpose. The Args and Returns sections are structured clearly, with no redundant sentences. However, it could be slightly more concise by integrating the parameter explanations into a single sentence, but overall, it's efficient and well-organized.

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 a submission tool with no annotations, 2 parameters (one required), 0% schema coverage, and no output schema, the description is incomplete. It lacks details on behavioral traits, parameter constraints, and return value specifics. For a tool that performs a write operation, more context is needed to ensure safe and correct usage, such as error conditions or submission limits.

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?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It adds some meaning by explaining 'df' as 'dataframe containing predictions data' and 'slot' as 'Submission slot number (1-based, default: 1)', which clarifies the purpose and default value. However, it doesn't detail the JSON format for 'df' or constraints on 'slot' (e.g., valid ranges), leaving significant gaps in parameter understanding.

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: 'Submit predictions from a JSON representation of a dataframe.' It specifies the verb ('submit'), resource ('predictions'), and source format ('JSON representation of a dataframe'), which is specific and actionable. However, it doesn't explicitly differentiate from its sibling 'submit_predictions_from_file', which handles file-based submissions versus JSON dataframes.

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 the sibling tool 'submit_predictions_from_file' for file-based submissions or other related tools like 'get_submission' or 'list_submissions'. There's no context on prerequisites, such as needing to have predictions ready in JSON dataframe format, or exclusions for when not to use it.

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