Data Analytics MCP Toolkit

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 action ('compute silhouette score') but lacks details on what this entails: e.g., whether it's a read-only operation, if it modifies data, performance characteristics, or output format. For an evaluation tool with zero 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 a single, efficient sentence: 'Compute silhouette score for a clustering model on test data.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes essential information.

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 complexity (evaluation operation with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the silhouette score metric, what the output looks like, error conditions, or dependencies on other tools (e.g., train_kmeans). For a tool with no structured support, the description should provide more context to be fully helpful.

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 schema provides no parameter descriptions. The description adds no information about the parameters (model_id, test_data_id, session_id), such as what they represent, expected formats, or how they relate to the computation. It fails to compensate for the low coverage, leaving parameters largely undocumented.

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: 'Compute silhouette score for a clustering model on test data.' It specifies the verb ('compute'), resource ('silhouette score'), and scope ('clustering model on test data'), which distinguishes it from siblings like evaluate_classification or evaluate_regression. However, it doesn't explicitly differentiate from all siblings (e.g., run_analytics might also involve evaluation), 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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a trained clustering model), when not to use it (e.g., for non-clustering models), or direct alternatives among siblings. The context is implied (evaluation after training), but no explicit usage rules are given.

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