mcp-csv-analyst

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it describes the basic operation (getting unique values with counts), it doesn't mention important behavioral aspects: whether this is a read-only operation, what happens with large datasets, how missing values are handled, or what the output format looks like. For a data processing tool with zero annotation coverage, this represents significant gaps in behavioral transparency.

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 purpose. There's zero wasted language, and the information is front-loaded with the core functionality. Every word earns its place in this minimal but complete statement of what the tool does.

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 moderate complexity (data processing with 3 parameters) and 100% schema coverage but no annotations or output schema, the description is minimally adequate. It states what the tool does but doesn't provide context about output format, error conditions, or performance characteristics. For a tool that processes CSV files and returns statistical information, more context about the return structure would be helpful since there's no output schema.

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?

The schema description coverage is 100%, with all three parameters well-documented in the input schema. The description doesn't add any parameter semantics beyond what's already in the schema - it doesn't explain the relationship between parameters or provide usage examples. With complete schema coverage, the baseline score of 3 is appropriate since the schema does the heavy lifting for parameter documentation.

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: 'Get unique values in a column with their counts' - a specific verb ('Get') and resource ('unique values in a column'). It distinguishes from siblings like csv_aggregate or csv_group_by by focusing on unique value extraction rather than aggregation or grouping operations. However, it doesn't explicitly mention CSV file processing, though this is implied by the tool name and context.

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. With siblings like csv_aggregate, csv_filter, and csv_group_by available, there's no indication of when csv_unique is appropriate versus when other tools might be better suited for similar data analysis tasks. The description lacks any 'when-to-use' or 'when-not-to-use' context.

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