list_offers
Lists active, agent-discoverable public Masterminds HQ offers.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Lists active, agent-discoverable public Masterminds HQ offers.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, covering safety. The description adds scoping context (active, public, agent-discoverable) but does not disclose additional behaviors like pagination, ordering, or what constitutes 'active' or 'agent-discoverable'. No contradiction with annotations.
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 sentence with no wasted words. The verb is front-loaded, and every word contributes meaning, making it highly efficient.
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 0-parameter listing tool, the description is adequate. However, without an output schema, it does not describe the structure of returned offer objects, which would improve completeness. The read-only annotation and simple nature of the tool keep this from being a larger gap.
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 tool has 0 parameters and schema coverage is 100%. With no parameters to explain, the baseline is 4, and the description correctly avoids adding unnecessary parameter details.
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 uses a specific verb ('Lists') and clearly identifies the resource ('active, agent-discoverable public Masterminds HQ offers'). This distinguishes it from siblings like get_offer and recommend_offer, making the purpose unmistakable.
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 implies usage (use this to list offers) but does not explicitly state when to use this tool versus alternatives such as get_offer for a single offer or list_events for events. No exclusions or alternative tools are mentioned.
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.
Tools are mostly distinct: get_offer vs list_offers vs recommend_offer target different granularities, and get_public_proof vs search_public_proof follow the same pattern. The only potential confusion is between create_checkout_link and prepare_purchase_intent, but their descriptions clarify different purchase mechanisms.
All tools follow a consistent verb_noun pattern in snake_case (e.g., list_offers, create_checkout_link, search_faqs). No mixing of conventions or vague verbs.
10 tools is well within the ideal 3-15 range for a platform focused on offers, events, FAQs, proofs, and purchases. Each tool serves a clear purpose, and the count feels neither sparse nor bloated.
The tool surface covers offer discovery, retrieval, recommendation, purchase initiation, events, FAQs, proofs, and system capabilities. Minor gaps exist (e.g., no get_event or get_faq), but core workflows are fully supported for an agent-facing public server.