Detect AI-generated content using dual-signal analysis combining rewrite-distance and classifier scoring. Returns 4-tier classification with per-sentence signals and analysis.
Detect AI-generated content using dual-signal analysis: Raidar rewrite-distance method + classifier. Returns 4-tier classification with per-sentence analysis and detected signals. (~5 credits)
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| esl_mode | No |
Changes observed during successful MCP inspections.
v1.0.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the return shape (4-tier classification with per-sentence analysis and signals) and cost (~5 credits), which are valuable. However it doesn't specify input limits, whether results are deterministic, or what esl_mode changes behaviorally.
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 compact sentences, front-loaded with the core action and method, followed by output and cost. Efficient; only minor room to specify parameters.
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 no output schema and no annotations, the description covering return tiers and cost is helpful, but leaves key gaps: parameter meaning (especially esl_mode), input constraints, and usage context. Adequate but incomplete for a detection tool.
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 coverage is 0% – both parameters have empty descriptions. The description mentions 'per-sentence analysis' but does not explain 'text' or 'esl_mode' (which likely adjusts detection for ESL writers). It fails to compensate for the coverage gap.
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?
States a specific verb (Detect) and resource (AI-generated content) and names the underlying methods (Raidar rewrite-distance + classifier). This is clearly distinguishable from all siblings, none of which do AI-detection.
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 on when to use this versus alternatives, nor on the esl_mode parameter's role, which is the natural 'when' lever here. The description only explains what it does, not the conditions for selecting it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.