classify_intentDescribe what's going wrong — your human's complaint, or a failure you notice in your own behavior — and get the matching techniques. Deterministic matching; if the description fits two problems it returns one clarifying question instead of guessing.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | The problem as experienced, in plain words — symptoms, not solution guesses |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits: deterministic matching and handling of ambiguity by returning a clarifying question. Since no annotations are provided, the description carries full responsibility for transparency, which it fulfills well.
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 two sentences long, front-loaded with the purpose, and every sentence adds value without redundancy. No unnecessary words.
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 the tool's simplicity (1 parameter, no annotations, no output schema), the description is fully complete: it explains purpose, when to use, behavioral details, and parameter semantics. There are no gaps.
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?
The schema already describes the parameter ('symptoms, not solution guesses'), and the description adds practical guidance ('in plain words — symptoms, not solution guesses'). With 100% schema coverage, the description adds meaningful context beyond the schema.
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 clearly states the tool's purpose: 'Match a problem description to techniques'. The verb 'match' and the resource are specific, and it distinguishes from siblings like 'find_technique' or 'apply_technique' by focusing on classification.
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 explicitly tells when to use the tool ('Describe what's going wrong — your human's complaint, or a failure you notice in your own behavior') and explains behavior in ambiguous cases ('returns one clarifying question instead of guessing'). However, it does not mention when not to use it or direct alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct purpose: listing categories, classifying intent, finding/getting technique details, and applying a technique. There is no overlap, and descriptions clearly differentiate them.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., apply_technique, list_categories). The naming is uniform and predictable.
With 5 tools, the server is well-scoped for its focus on technique selection and application. Each tool serves a necessary step in the workflow without unnecessary bloat.
The tool surface covers the full workflow: browsing categories, classifying intent, retrieving technique details, and applying techniques. There are no obvious gaps for an agent using this server.