Extract the most frequent keywords from input text and return them with their frequencies as JSON.
Extract keywords (most common words) from text.
Args: text: The input text count: Number of keywords to return (default 5)
Returns: JSON string with keywords and frequencies
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| count | No |
| 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 provided, so description carries full burden. Mentions output as JSON string with keywords and frequencies, but lacks details on edge cases (e.g., stop words, case sensitivity, handling of non-words).
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?
Extremely concise—three short lines covering purpose, parameters, and return type. No superfluous information.
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?
Provides sufficient context for a simple text analysis tool: purpose, parameters, and return format are all mentioned. Slight gap on the exact structure of the JSON output, but not critical for basic usage.
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?
Both parameters (text, count) are described in the schema and the description, achieving 100% schema coverage. Description adds minimal meaning beyond schema—'most common words' clarifies text processing, but count semantics are surface-level.
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?
Clearly states it extracts keywords (most common words) from text, which is a specific verb+resource. Distinct from sibling tools like sentiment analysis or summarization.
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?
Usage is implied rather than explicitly stated—if the user needs keywords, this tool is the obvious choice. No explicit comparison to sibling tools or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.