Score readability

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. It goes beyond a simple one-liner by explaining not only the metrics (Flesch ease, grade level) but also that it provides a plain-language interpretation of the number and reports the longest sentence as a likely cause of poor readability. This gives the agent a clear picture of what the tool does and what it outputs, though it does not mention every detail (e.g., range of scores or edge cases).

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 two sentences with zero filler. The main purpose is front-loaded ('Score how hard a piece of text is to read'), followed by the specific metrics and an additional behavior (longest sentence). Every sentence earns its place and the structure is highly efficient.

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 there is no output schema, the description does a good job of explaining what the tool returns: Flesch score, grade level, plain language interpretation, and longest sentence. This is sufficient for an agent to understand the tool's behavior and call it correctly. It could be more complete by specifying numeric ranges or examples, but those are not strictly necessary for correct invocation.

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 100%: the only parameter 'text' is described as 'The prose to score.' The description adds minimal semantic value beyond this, only referring to 'a piece of text' and 'prose' again. Since the schema fully documents the parameter, a baseline score of 3 is appropriate.

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 function: scoring how hard a text is to read, with specific metrics (Flesch reading ease and grade level). It also distinguishes itself from all sibling tools (count, diff, regex, hash) by focusing on readability assessment. The verb 'score' and resource 'a piece of text' are specific and unambiguous.

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 implies usage through its purpose: use this when you need readability metrics for prose. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or specific conditions. Since the sibling tools are clearly different (counting, diffing, regex testing, hashing), the intended use is inferable but not explicitly articulated.

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