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 mentions the return format (JSON), it doesn't address important behavioral aspects like error handling (e.g., what happens if the file doesn't exist or column isn't found), performance characteristics, memory usage with large files, or whether the operation modifies the original CSV file. The description provides minimal behavioral context beyond the basic operation.

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 at just two sentences that directly state the core functionality and output format. Every word earns its place with zero redundancy or fluff. The information is front-loaded with the primary purpose stated immediately.

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 tool with 7 parameters, no annotations, and no output schema, the description is minimally adequate. It covers the basic operation and output format but lacks important context about error conditions, performance, and how this tool differs from sibling CSV tools. The absence of output schema means the description should ideally say more about the return structure, but it only mentions the format (JSON), not the content.

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 100% schema description coverage, the input schema already documents all 7 parameters thoroughly. The description doesn't add any meaningful parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how sort_by interacts with filtering) or provide usage examples. The baseline score of 3 reflects adequate but not enhanced 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: 'Filter CSV rows by a column condition' specifies the verb (filter) and resource (CSV rows), and 'Returns matching rows as JSON' indicates the output format. However, it doesn't explicitly differentiate from sibling tools like csv_sample or csv_unique, which may also involve row selection operations.

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_group_by, and csv_sample available, there's no indication of when filtering by column condition is preferred over other CSV manipulation approaches. The description lacks any context about use cases or prerequisites.

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