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 full burden. It mentions the tool creates a box plot but lacks critical behavioral details: it doesn't specify if this is a read-only operation, what happens to the plot (e.g., displayed, saved, returned as data), whether it modifies data, or any performance considerations. The description is too sparse for a tool with potential side effects like visualization output.

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 extremely concise—a single sentence—with zero wasted words. It front-loads the core functionality ('Box plot') and efficiently explains key parameter behavior. Every part earns its place, making it easy to scan and understand quickly.

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 (a visualization tool with 4 parameters), no annotations, and no output schema, the description is incomplete. It lacks details on what the tool returns (e.g., plot object, file path, or nothing), error conditions, dependencies on other tools like load_data, and how it fits into the broader analytics workflow. This is inadequate for guiding an agent in a multi-tool environment.

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. It explains the 'column' parameter's behavior (single numeric column or all if omitted), adding meaning beyond the schema's basic type definitions. However, it doesn't cover other parameters like data_id, title, or session_id, leaving them undocumented. With 4 parameters total and only partial coverage, this meets the baseline for moderate schema coverage gaps.

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 creates a box plot visualization from numeric data, specifying 'single numeric column, or all numeric columns if column is omitted.' This is a specific verb+resource combination that distinguishes it from siblings like plot_bar or plot_histogram. However, it doesn't explicitly mention data visualization or differentiate from all siblings like plot_heatmap in terms of statistical vs. correlation focus.

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 minimal guidance: it implies usage when you want a box plot from numeric data, but offers no explicit when-to-use advice, prerequisites (e.g., data must be loaded first), or alternatives among siblings. For example, it doesn't clarify when to choose plot_box over plot_histogram for distribution analysis or how it relates to evaluation tools like evaluate_regression.

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