Analyze text

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

With no annotations, the description carries the full behavioral burden. It usefully flags that the sentence count is approximate and that the output is deterministic statistics plus a hash, but it does not state that the operation is read-only/non-mutating, nor how the hash is encoded (hex, casing), which matters for a checksum tool.

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?

Two sentences, no filler; the enumerated output list is front-loaded and the usage cue follows. Every clause earns its place.

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?

There is no output schema, and the description compensates by enumerating exactly what is returned, including the approximation caveat. What remains missing is error/edge behavior (empty input, encoding assumptions), but nothing essential to calling the tool is absent.

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?

There is a single parameter and schema description coverage is 100%, so the schema already defines 'text' fully. The description adds no syntax or format detail beyond it, making this the baseline case where structured fields do the work.

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 gives a specific verb ('compute exact statistics') plus the resource (a block of text) and enumerates the exact outputs (characters, words, lines, sentences, SHA-256 hash), which clearly separates it from the read/write file and note siblings. It never names the closest sibling, transform_text, so an agent must still infer the boundary between analysis and transformation.

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?

"Useful whenever an exact count or checksum matters" is an implied usage cue, but it gives no explicit when-to-use/when-not-to-use guidance and does not point to transform_text or any alternative for related tasks. Adequate context, no routing information.

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