get_public_proof
Fetches a single verified public proof record by ID.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The public proof ID |
Fetches a single verified public proof record by ID.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The public proof ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already communicates that this is a safe read operation. The description adds useful context like 'public' and 'verified', but it does not disclose potential behaviors such as error cases, whether the record might be absent, or any additional response details.
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 that immediately states the action, resource, and scope. There is no filler, repetition, or unnecessary detail.
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 one-parameter read operation with a readOnlyHint annotation, this description is largely sufficient for tool selection and invocation. It does not include return format or error behavior, and there is no output schema to fill that gap, but the low complexity keeps this from being a significant deficiency.
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 already fully documents the single 'id' parameter with the description 'The public proof ID' (100% coverage). The description's phrase 'by ID' adds no new semantic information beyond what the schema provides, so the baseline score of 3 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?
The description uses a specific verb ('Fetches') tied to a clear resource ('single verified public proof record by ID'), and explicitly scopes the operation to lookup by ID. This differentiates it from the sibling search_public_proof, which implies search-like behavior rather than direct fetch.
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 'by ID' phrasing implies this tool should be used when the agent already knows the public proof ID. However, it does not explicitly mention alternatives like search_public_proof or state when not to use this tool, so the guidance remains implicit.
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.