mistral_classify_textClassify text using a fine-tuned classifier model. Submit one or more texts with a chosen model to assign categories and organize content automatically.
Classify text using a fine-tuned classifier.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Classifier model | |
| inputs | Yes | Texts to classify |
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint and destructiveHint=false, so the safety profile is covered. The description adds nothing beyond annotations about behavior, and with no output schema it does not say what a classification result looks like (labels, scores, per-text results).
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?
A single efficient sentence with no filler, front-loading the verb and resource. It is arguably underspecified rather than wasteful, but structurally clean.
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?
For a simple 2-parameter tool with full schema coverage and annotations covering safety, the definition is minimally sufficient. It leaves open what the classifier outputs and why a fine-tuned model is required, but these are minor for a single-purpose inference call.
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 parameters (model, inputs) are already documented in the schema. The description adds no extra meaning, which is the baseline 3 when the schema does the heavy lifting.
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 (classify) and resource (text) and adds the key qualifier 'using a fine-tuned classifier,' which separates it from mistral_moderate_text. It doesn't explicitly name which sibling to prefer, so differentiation is only implied.
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 mistral_moderate_text, mistral_embeddings, or mistral_chat_completion. The agent must infer the use case entirely.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.