Identifies the most frequent significant words in a text and returns them as keywords, with an optional limit for ranking. Helps surface key topics for summarization or analysis.
Return the most frequent significant words in text. PREMIUM: consumes 1 free-quota call.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to analyze | |
| limit | No | Max keywords (default 10) |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does disclose a genuine behavioral trait beyond the schema — the premium quota cost of one free-quota call — but says nothing about ordering, stopword handling, tie-breaking, or the shape of the returned data for a no-annotation 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 short sentences, purpose first and the cost warning second. Every sentence carries load and there is no 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 carry everything. It covers the core action and cost, but omits the return format (ranked list? tuples with counts?) and how 'significant' is defined, which an agent would need to consume results correctly.
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%, so both the required text and the optional limit (with its default of 10) are already documented in the schema. The description adds no additional semantics such as what counts as a 'significant' word, so the baseline of 3 applies.
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 specific verb and resource: returning the most frequent significant words in a text. That distinguishes it meaningfully from word_count (counting all words) but does not explicitly position it against summarize or word_count, leaving sibling differentiation to inference.
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?
There is no guidance on when to pick this tool over word_count or summarize, nor any preconditions such as text length. The only usage-adjacent signal is the cost note, which is a constraint rather than routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.