Calculate word counts and extract key linguistic insights from any body of text. Determine the most frequent words quickly for immediate textual analysis.
Word count plus the most frequent words of a text. Example of a pure, no-I/O tool: instant, deterministic, safe.
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | ||
| text | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose real behavioral traits - pure, no I/O, instant, deterministic, safe - which is useful. However, it omits edge-case behavior (empty input, tokenization/case rules) and any note on how results are ordered or bounded.
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 short sentences, front-loaded with the core capability. The second sentence earns partial credit by conveying the no-I/O/deterministic profile, though it reads as documentation boilerplate rather than agent-facing guidance.
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 formatting need not be described. But with 0% parameter coverage and no annotations, the description leaves the 'top' parameter and input expectations entirely undocumented - the agent must guess from the schema defaults alone.
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% and neither parameter is named in the description. 'Most frequent words' implicitly suggests a top-N limit but never states that the 'top' parameter controls it, its default of 5, or what 'text' must look like.
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 names a specific verb+resource: word count plus most-frequent-word extraction from a text. It is unambiguous what the tool computes. It does not differentiate from siblings, but the only sibling (pypi_project) is unrelated, so no meaningful confusion exists.
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 offers no when-to-use guidance, no alternatives, and no conditions or exclusions. 'Example of a pure, no-I/O tool' is meta-commentary about the tool's character, not routing guidance for an agent choosing between tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.