get_usage[META] Your own API usage: total calls, per-day series and top endpoints over period '7d' or '30d'. Use it to budget calls — free tier check_trade is 3/day (get_check_history shows the remaining count). Mirrors REST /usage. Private to your account.
| Name | Required | Description | Default |
|---|---|---|---|
| period | No | 7d |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds useful context: the data is private to the account, mirrors REST /usage, and covers specific metrics. It doesn't describe return format or pagination, but for a simple stats tool with read-only annotation, the added context is sufficient; no contradiction.
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 three sentences with a clear [META] tag. It front-loads the core information, then adds usage guidance and a developer reference. Every sentence contributes value without padding, making it both concise and well-structured.
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 single-parameter tool with no output schema, this description covers all necessary context: what is returned, the period options, the budgeting use case, privacy, and an API reference. It is complete enough for an agent to select and invoke the tool correctly without further information.
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 only provides the parameter name 'period' with a default, but no description. The description compensates by explicitly listing the allowed values ('7d' or '30d'), which is essential for correct invocation. This fully fills the gap left by the 0% schema coverage.
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 returns the user's own API usage data (total calls, per-day series, top endpoints) for a given period, with the [META] tag distinguishing it from the many market-data siblings. It specifies the resource ('your own API usage') and the action ('get usage'), making the 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?
Explicitly tells when to use it: 'Use it to budget calls' and references the free tier limit of check_trade (3/day) with get_check_history for remaining count. This provides a clear use case and points to related tools, going beyond what the schema or annotations alone would convey.
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, but there are clusters of similar concepts (e.g., get_liquidity_map vs get_liquidation_map, get_state vs get_state_brief, multiple signal-related tools) that could cause misselection despite thorough documentation.
All tools follow a consistent lowercase verb_noun pattern, predominantly get_* nouns, with only a few non-get verbs like list_signals, rank_trades, log_trade, etc., but the style is uniform.
With 52 tools, the surface is extremely heavy for an agent to navigate. While the server's scope is broad, the count far exceeds the typical 3-15 range and falls into the 'too many' category.
The tool set covers the full lifecycle for journaling, signals, market analysis, and proof, with no major dead ends. Minor gaps exist, such as no dedicated get_trade_by_id (workaround via get_journal) and no get_market_state tool despite being referenced in get_state.