regenerate_briefRe-run the MiniMax/Anthropic brief generator from the project's current intake_responses and persist the result. Flips status to brief_ready on success or brief_failed on error. Use after the customer edits answers post-submit, or when the first AI attempt failed.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | project id to re-run the brief for |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Input schema / properties / id / descriptionAdded value: +"project id to re-run the brief for"Output schema / (root)Previous value: -nullNew value: +{
+ "$schema": "http://json-schema.org/draft-07/schema#",
+ "additionalProperties": false,
+ "properties": {
+ "result": {}
+ },
+ "required": [
+ "result"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses side effects: flips status to brief_ready or brief_failed. Annotations already indicate non-idempotent and open-world; description adds specific behavioral context without contradiction.
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 succinct sentences: first states action and effect, second gives usage guidance. No wasted words, front-loaded with key 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?
For a simple tool with one parameter and known side effects, the description covers the main purpose and usage. With an expected output schema (not shown), return values are not required. Could mention input constraints like required permissions, but overall adequate.
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 single parameter 'id' is already well-described in the input schema (100% coverage). The description adds no further semantic meaning beyond what the schema provides, so baseline score applies.
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 the tool regenerates an AI brief from intake_responses and persists the result. Distinguishes from sibling tools like update_brief and create_project by specifying the AI-driven regeneration from existing responses.
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?
Explicitly says when to use: after customer edits answers or when first AI attempt failed. Provides clear context for usage, though it does not explicitly mention alternatives from the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.