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 what the tool does (creates a scatter plot) but doesn't describe how it behaves: e.g., whether it displays the plot, saves it to a file, returns an image, requires specific data formats, or has any side effects. For a visualization tool with zero annotation coverage, this is a significant gap.

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 that directly states the tool's function. It's front-loaded with the core action and avoids any unnecessary words. Every part of the sentence earns its place by specifying the plot type and key parameters.

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 data visualization tool with 5 parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., a plot object, file path, or nothing), how errors are handled, or dependencies on other tools like load_data. This leaves significant gaps for an AI agent to use it correctly.

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 for undocumented parameters. It mentions x_column and y_column, which are two of the five parameters, but doesn't explain data_id, title, or session_id. The description adds minimal value beyond the schema, failing to clarify what these parameters mean or how they should be used.

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: creating a scatter plot with specific x and y columns. It uses the verb 'plot' and specifies the resource (data columns), distinguishing it from siblings like plot_bar or plot_line by mentioning the scatter plot type. However, it doesn't explicitly differentiate from other visualization tools beyond the plot type.

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 when scatter plots are appropriate compared to other plot types (e.g., plot_line for trends, plot_histogram for distributions) or other tools like evaluate_regression for analysis. There's no context about prerequisites or typical use cases.

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