analyze_text

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

No annotations are provided, so the description carries the full disclosure burden. It discloses that results come as a JSON string, but does not say what statistics are produced, whether the text is processed whole or tokenized, or any edge-case behavior (empty text, long input). For a text-processing tool with zero annotation coverage, this is a meaningful 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 docstring is compact with clean Args/Returns sections and front-loads the purpose in the first sentence. The Args section does repeat the schema, but the Returns clause adds useful information about the JSON string output. No wasted sentences.

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?

An output schema exists, so return values need not be spelled out, and there is only one self-evident parameter. However, the core ambiguity of what 'statistics' means remains unaddressed, and with no usage guidance, the description is only minimally complete for such a simple tool.

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. It adds 'The input text to analyze' for the text parameter, but this essentially restates what the schema title 'Text' already implies. The parameter is self-evident, so the added value is minimal yet adequate for a single obvious parameter.

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 states a verb ('analyze') and a resource ('text') and mentions 'statistics' as the output, but 'statistics' is vague and undefined. With siblings like analyze_sentiment, summarize_text, and extract_keywords all analyzing text, this description does not specify which statistics (word count, character count, frequency) are computed, so it does not distinguish itself from its siblings.

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?

No guidance is given on when to use this tool versus the eight siblings. The description does not state exclusions, alternatives, or context for choosing it over analyze_sentiment or check_reading_level. An agent has no way to route between these tools from the description alone.

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