Re-read an order intent created by create_order — the recorded basket and its honest status. Payment truth for checkout purchases lives with Stripe (the payer's receipt is authoritative); metered work is receipted on your meter account and readable via get_account.
| Name | Required | Description | Default |
|---|---|---|---|
| order_id | Yes | The UUID returned by create_order |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the operation is a re-read, specifies the output (basket and honest status), and adds important context about payment truth residing with Stripe. It does not mention side effects, but none are expected for a read operation.
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 sentences with no wasted words. The first sentence states the core purpose, and the second provides necessary caveats. Information is front-loaded and easy to scan.
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 read-by-id tool with one parameter and no output schema, the description adequately explains what the tool returns ('recorded basket and its honest status') and provides essential caveats. No additional information seems necessary for an agent to invoke it correctly.
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?
Schema coverage is 100% and the parameter is documented as 'The UUID returned by create_order'. The description adds no significant new meaning beyond the schema, merely echoing 'created by create_order'. Baseline 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 're-read' and identifies the resource as 'an order intent created by create_order', which clearly indicates its function. It also distinguishes itself from siblings by stating that payment truth and metered work receipts reside elsewhere.
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 provides clear context for when this tool is appropriate (reading an order intent) and explicitly notes alternative sources for payment truth (Stripe) and metered work (get_account). However, it does not explicitly state 'use this when you need the basket', relying on inference.
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 targets a distinct function: ask is async event queries, get_answer retrieves results, get_signals and top_accelerating provide different signal views, get_entity and search_entities cover entity details and search, etc. No two tools have overlapping purposes, and descriptions clearly separate them.
The majority of tools follow a 'get_' prefix for retrieval (13 of 21) and other clear verbs like 'ask', 'create_order', 'propose_topic', 'send_feedback'. A few use noun phrases like 'todays_brief' and 'top_accelerating', which breaks the pattern slightly but are still intuitive. Overall consistent and predictable.
21 tools cover the full scope of the server: exploration, querying, ordering, account management, feedback, and data records. Each tool serves a clear purpose without redundancy, and the count feels well-scoped for the domain.
The tool surface covers all major workflows: browsing signals, asking questions, getting detailed reports, ordering, providing feedback, and proposing new topics. Minor gaps exist (e.g., no explicit tool to cancel an order or manage watched topics directly), but core operations are present and agents can handle the domain effectively.
