Read a demo identity record, including credits, open shots and recent settlements. Poll this after expiry because settlement is lazy and this read collects it.
| Name | Required | Description | Default |
|---|---|---|---|
| raw | No | return the full state instead of the compact projection | |
| handle | Yes | handle returned by ratchet_new_demo |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral disclosure. It explicitly notes a non-obvious side effect: 'this read collects' lazy settlement, implying the read can mutate state by finalizing settlements. This goes beyond a simple read and is essential for an agent to know. It doesn't mention auth requirements or further side effects, but for a read operation 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?
Two tightly written sentences with no redundant words. The first sentence states precisely what the tool does, and the second gives a practical usage hint. Every sentence earns its place.
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 read tool with only two parameters and no output schema, the description conveys the purpose, what it returns (contents list), and when to use it. It could mention error handling or the exact return format, but given the low complexity, this is sufficiently complete for an agent to call 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 description coverage is 100%, with both 'handle' and 'raw' already described in the input schema. The tool description adds no additional meaning beyond what the schema provides, so the baseline of 3 is appropriate when the schema documents all parameters.
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 starts with a specific verb 'Read' and names the exact resource 'demo identity record', then enumerates its contents (credits, open shots, recent settlements). This clearly distinguishes it from the creation tool 'ratchet_new_demo' and other read tools in the sibling list, such as 'ratchet_agent_record' or 'ratchet_board'.
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 a concrete trigger scenario ('Poll this after expiry') and the reasoning behind it ('settlement is lazy and this read collects it'). It does not explicitly name alternatives or exclusion conditions, but the context is clear enough for an agent to decide when to invoke it.
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.
Every tool targets a distinct resource or workflow: board, arena, Pyth context/path, proof, demo identity, ranked submission, invitations, and challenges. Even the many read-style tools are clearly separated by what they return, so an agent should not confuse them.
All tools use the ratchet_ prefix and snake_case, which makes them easy to group and predict. The pattern is not uniformly verb_noun—several tools are noun-style reads like ratchet_board and ratchet_arena—but the naming is still consistent and readable.
Thirteen tools is well within a reasonable scope for a forecasting arena. The tools cover context reads, demo workflows, ranked workflows, invitations, challenges, and public proofs without feeling padded or redundant.
The demo lifecycle is well covered with create, shoot, and state reads, and ranked prepare/submit gives a clear entry path. However, there is no ranked-state read to inspect open shots, balances, or settled results after submission, and challenges are read-only despite implying create/accept actions should exist.