readability_scoreMeasure readability of any URL or raw text using Flesch-Kincaid, SMOG, and Coleman-Liau scores to compare with competitors and identify content complexity gaps.
Flesch-Kincaid, SMOG, and Coleman-Liau readability metrics for a URL or raw text.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | ||
| text | No |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior, but it only mentions inputs. It omits whether the operation is read-only, what the output structure is, potential failure modes, or any side effects, leaving the agent with an incomplete safety and behavior picture.
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 a single, concise sentence that front-loads the key metrics and input types with no filler. Every word contributes to understanding the tool's core function.
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 no annotations, so the description carries the burden of explaining return values, parameter requirements, and usage constraints. It fails to state what the tool returns, whether url or text is required, or any prerequisites, making it insufficient for a confident 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 0%, so the description must compensate. It clarifies that url and text are the two accepted input forms, which adds meaning beyond the bare schema. However, it doesn't explain whether they are mutually exclusive, optional, or how they relate to each other, leaving room for ambiguous invocation.
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 explicitly names three readability metrics (Flesch-Kincaid, SMOG, Coleman-Liau) and the two input types (URL or raw text), making the tool's function unmistakable. It doesn't differentiate itself from siblings like content_quality_score, but the specific metrics make its purpose clear.
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?
No guidance is given on when to choose this tool over alternatives such as content_quality_score or extract_keywords. The description states only what the tool does, not the context in which it should be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.