Data Analytics MCP Toolkit

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden but only states what the tool does, not behavioral traits. It doesn't disclose whether this is a read-only operation, if it modifies data, what permissions are needed, or how results are returned (e.g., format, potential errors).

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 that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every word contributing to understanding.

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 no annotations, 0% schema coverage, no output schema, and three parameters, the description is incomplete. It lacks details on parameter meanings, behavioral context (e.g., safety, output format), and usage guidelines, making it inadequate for a tool with this complexity.

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 but adds no parameter information. It doesn't explain what model_id, test_data_id, or session_id represent, their formats, or how they relate to the computation, leaving all three parameters 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 action ('Compute') and the specific metrics (MSE and R²) for a regression model on test data. It distinguishes from siblings like evaluate_classification and evaluate_clustering by specifying regression, but doesn't explicitly contrast with them.

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?

No guidance is provided on when to use this tool versus alternatives like evaluate_classification or evaluate_clustering. The description implies usage with regression models but doesn't specify prerequisites (e.g., needing a trained model and test data) or exclusions.

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