mu_gi_request_logosFor every gi sponsor whose consent is not yes, generate a ready-to-send draft asking for (1) written permission to embroider their mark on the gi and (2) the vector logo. Does NOT send (outbound mail is a human gate). When a brand grants permission, record it with mu_gi_logo_set consent:yes. Owner-only.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses key behaviors: owner-only access, generates drafts only, does not send, and specific request content. Does not mention side effects like overwriting existing drafts, but for a 0-parameter tool, this is sufficient.
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?
Extremely concise: two sentences cover purpose, condition, content, exclusions, and follow-up action. No filler, front-loaded with the main action.
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 zero parameters and no output schema, the description fully covers what the agent needs: target sponsors, draft content, what not to expect, and next steps. References sibling tool for recording consent, providing complete workflow context.
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?
No parameters exist, so baseline score of 4 applies. The description does not need to add parameter info as the schema is already complete and empty.
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?
Description clearly states the tool generates draft emails for logo permission requests from sponsors without consent. It specifies the request content (written permission and vector logo) and distinguishes itself by being owner-only and not a sending tool.
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: for sponsors whose consent is not 'yes'. Provides after-action guidance to use mu_gi_logo_set to record permission. Also clarifies it does not send emails, setting expectations correctly. Could be improved by mentioning when not to use, but still effective.
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 clearly distinct purpose; no two tools appear to do the same thing. Even closely related tools (e.g., mu_quote vs mu_rfq_create, mu_preview_mockup vs mu_create_product) are differentiated by read-only vs. action, or draft vs. send.
Tool names consistently use snake_case with the mu_ prefix, and follow a clear verb_noun or noun_verb pattern. Groups like mu_gi_*, mu_ship_*, mu_rfq_* maintain internal consistency. No arbitrary or ambiguous names.
The tool count of 28 is on the higher side but appropriate for the breadth of functionality (registration, product lifecycle, manufacturing, shipping, sales, admin). It's well within a manageable range for a comprehensive server.
The tool surface covers core workflows comprehensively: registration, product CRUD (with create, update, retire), manufacturing quotes (informational and RFQ with spec drafting), shipping (CSV, tracking, status updates), sales data, and gi-specific management. Minor gaps include lack of store update/delete and product search, but these are not critical for the main use cases.