Chainlink-vs-Uniswap pool price deviation (bps, sorted by absolute value). status: ok (<100), warning (100-500), alert (>500). Large deviations are normal in low-liquidity pools — check liquidity_usd.
| Name | Required | Description | Default |
|---|---|---|---|
| min_abs_bps | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavior context: status categories and the note that large deviations are normal in low-liquidity pools, plus a hint to check liquidity_usd. It does not contradict 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?
The description is two sentences and immediately states the core metric and scope. The status thresholds and liquidity caveat are valuable additional context with no wasted words.
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-only tool with one optional parameter and no output schema, the description covers the main output fields (status, liquidity_usd) and sorting. However, it fails to explain the min_abs_bps parameter or the full return structure, so the agent has incomplete information for correct invocation and result interpretation.
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 schema has 0% description coverage and the description does not mention min_abs_bps at all. The parameter name is somewhat suggestive of a minimum absolute basis point filter, but the description does not explain how to use it or its effect on results, so the agent is left guessing despite the descriptive name.
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 clearly states the tool's metric (Chainlink-vs-Uniswap pool price deviation in bps) and its sorting order. It distinguishes from other pool and position tools in the sibling list, though it lacks an explicit verb like 'get' or 'list'.
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 gives no explicit when-to-use guidance or alternatives, but it does provide interpretation thresholds (ok/warning/alert) and a caveat about low-liquidity pools, which helps the agent decide how to act on results. This is implied usage context rather than explicit selection guidance.
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.
Most tools have clearly distinct purposes, with detailed descriptions that separate e.g. hood_basis_snapshot from hood_basis_history and perp_spot_basis. A few clusters like flow_summary vs token_flows and multiple gap/basis tools could still cause slight ambiguity, but the descriptions are strong enough to guide correct selection.
All tool names follow a consistent snake_case pattern with domain prefixes (hood_, agent_, competition_, partner_), and verbs are used predictably (report_, execute_, get_, etc.). There is no mixing of conventions or vague generic names.
39 tools is on the high side, but the server covers a very broad domain (market data, signals, trading execution, competition, partner APIs, agent reporting). While many tools earn their place, a few could be consolidated (e.g., gap/basis variants), making it feel heavier than necessary for typical usage.
The surface covers the core lifecycle: market data, signals, paper/real/partner trading, position tracking, and performance reporting. Minor gaps exist (e.g., no explicit wallet balance or order cancellation), but the core workflows are well-covered and derived endpoints fill most needs.
