Data Analytics MCP Toolkit

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the server picks pipelines and returns either charts or ML metrics, which is useful. However, it doesn't address critical behavioral aspects: whether this is a read-only or mutating operation, potential data size limits, execution time expectations, error handling, or authentication requirements. For a complex analytics tool with 12 parameters, this leaves significant gaps.

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 concise with two sentences that efficiently convey the core functionality. The first sentence establishes the high-level nature with clear examples, and the second explains the server's role and possible outputs. There's no wasted text, though it could benefit from slightly more structure given the tool's complexity.

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?

For a complex analytics tool with 12 parameters, no annotations, no output schema, and many sibling tools, the description is insufficiently complete. It doesn't explain the relationship with sibling tools, doesn't clarify what types of analysis are supported beyond the examples, doesn't address data format requirements beyond mentioning CSV/JSON, and provides minimal guidance on parameter usage. The agent would struggle to use this tool effectively without trial and error.

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?

With 0% schema description coverage and 12 parameters, the description must compensate but provides minimal parameter guidance. It mentions 'intent' and 'data_source' through examples but doesn't explain the purpose of the other 10 parameters like 'drop_na', 'normalize', 'test_ratio', or column-specific parameters. The description doesn't clarify how parameters interact or which are required for different analysis types.

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: it's a high-level analytics tool that takes natural language intent and data to perform analysis, with the server selecting appropriate pipelines. It distinguishes from siblings by being a general-purpose entry point rather than specific operations like 'plot_scatter' or 'train_linear_regression'. However, it doesn't explicitly contrast with all siblings like 'clean_data' or 'load_data'.

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 clear context for when to use this tool: for high-level analytics requests where you describe what you want in natural language. It implies this is the entry point for analysis tasks, with siblings being more specific implementations. However, it doesn't explicitly state when NOT to use it (e.g., for simple plotting or data loading) or name specific alternatives among the many siblings.

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