analyze_table

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

No annotations exist, so the description carries the full burden, and it discloses meaningful behavior: processing happens locally, the report is Chinese Markdown, the xlsx includes native interactive bar charts, and there is an anonymous monthly quota with a paid-key bypass. Gaps remain around size limits for base64 payloads, failure/error behavior for malformed files, and whether output is truncated.

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?

Front-loaded with the capability and output before the argument list, and the Args block is scannable with aligned labels. The opening sentence is dense with metric enumerations that could be trimmed, but every element is informative rather than filler.

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?

With no annotations and no output schema, the description must supply both behavior and returns — and it largely does: it names the output format (Chinese Markdown insights), the optional xlsx artifact, the metrics computed, and the quota/auth model. Only edge-case handling (bad input, size limits, quota-exceeded responses) is unaddressed.

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%, yet the Args block documents all six parameters with semantics beyond the names — file_base64 as raw content, file_url as a direct HTTPS link, filename used 'only to decide csv vs xlsx', charts as max category columns to chart, return_xlsx as an opt-in base64 workbook, and api_key as a quota bypass. This fully compensates for the empty schema.

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?

Specific verb+resource (analyze a CSV/XLSX table) with explicit scope: 'fully locally', and an enumerated set of computed metrics (shape, missing rate, duplicates, min/max/mean/median/sum/stddev, correlations, top frequencies). The `health` sibling offers no overlap, so no differentiation is needed. An agent knows exactly what this returns without opening the schema.

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?

Gives concrete selection guidance between the two input paths ('file_base64: preferred' vs 'file_url: alternative') and explains the quota model with the bypass (api_key for the anonymous monthly quota). It does not state when NOT to use this tool or alternative tools for the same job, but the input-path precedence and quota note are genuine, actionable context.

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