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 tool computes accuracy, implying a read-only evaluation, but doesn't cover critical aspects like whether it modifies data, requires specific permissions, handles errors, or returns detailed metrics beyond accuracy. For a 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 accuracy for a classification 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 earns its place by conveying 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 with 3 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'accuracy' entails (e.g., a single metric or detailed report), how results are returned, or any behavioral nuances. For a tool with no structured support, the description should do more to guide the agent.

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%, meaning none of the three parameters (model_id, test_data_id, session_id) are documented in the schema. The description doesn't add any parameter semantics—it doesn't explain what these IDs refer to, their formats, or how they relate to the computation. This fails to compensate for the low schema coverage, leaving parameters largely unexplained.

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 accuracy for a classification model on test data.' It specifies the verb 'compute accuracy' and the resource 'classification model on test data,' making it easy to understand what the tool does. However, it doesn't explicitly differentiate from siblings like evaluate_clustering or evaluate_regression, which lowers it from a perfect score.

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 model and test data), exclusions, or comparisons to sibling tools like evaluate_clustering or evaluate_regression. This lack of context leaves the agent to infer usage from the purpose alone.

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