mcp-csv-analyst

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 mentions the operation types but doesn't cover critical behaviors like error handling (e.g., for non-numeric columns), performance considerations (e.g., for large files), or output format (since no output schema exists). This leaves significant gaps for an agent to understand how the tool behaves beyond basic functionality.

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 front-loads the core purpose without unnecessary words. It directly states the action and key details (aggregate types and target), making it highly concise and well-structured for quick comprehension.

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 (aggregation operations on CSV data) and lack of annotations and output schema, the description is incomplete. It doesn't explain the return values (e.g., numeric result or error messages), handling of edge cases, or dependencies like file format requirements. For a tool with no structured behavioral data, this leaves the agent under-informed about critical operational aspects.

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 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value by listing the aggregation operations, which are already in the enum in the schema. It doesn't provide additional context like examples or constraints beyond what's in the schema, meeting the baseline for high coverage but not enhancing understanding.

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 verb 'compute' and the resource 'aggregate on a numeric column', with specific operations listed. It distinguishes from siblings like csv_describe or csv_filter by focusing on aggregation rather than description or filtering. However, it doesn't explicitly contrast with csv_group_by, which might also involve aggregation, making it slightly less specific.

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 implies usage when needing to compute aggregates on numeric columns in CSV files, but doesn't explicitly state when to use this tool versus alternatives like csv_group_by (which might handle grouped aggregations) or csv_sample (which might provide summaries). No exclusions or prerequisites are mentioned, leaving usage context somewhat inferred rather than clearly defined.

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