get_offer
Retrieves detailed public offer metadata by offer_id.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| offer_id | Yes | The public offer identifier |
Retrieves detailed public offer metadata by offer_id.
| Name | Required | Description | Default |
|---|---|---|---|
| offer_id | Yes | The public offer identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true, so the read-only nature is already declared. The description adds that the metadata is 'public', which indicates no sensitive data, but it does not disclose behavior for missing or invalid IDs, pagination, or response structure. Some value is added beyond annotations, but context is thin.
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 a single sentence, front-loaded with the main verb and resource. Every word contributes to the meaning, with zero repetition or filler. This is an excellent model of conciseness.
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?
The tool is simple (one parameter, read-only), but there is no output schema and the description only says 'detailed public offer metadata' without specifying what fields are included. It does not cover error handling or edge cases, leaving an agent with partial understanding of what to expect.
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 input schema has 100% coverage: offer_id is described as 'The public offer identifier'. The description merely echoes 'by offer_id' without adding additional semantics like format, source, or required preconditions. With high schema coverage, a baseline of 3 is appropriate.
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 the specific verb 'Retrieves' and clearly identifies the resource as 'detailed public offer metadata by offer_id'. This distinguishes it from sibling tools like list_offers, which likely returns multiple offers, and makes the tool's purpose unambiguous.
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 when you have a specific offer_id and need its metadata, but it does not explicitly state when to use this tool versus alternatives such as list_offers or search_public_proof. No exclusions or comparative guidance are provided.
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.