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, so the safety profile is known. The description adds valuable behavioral context: it is private to the account, supports periods '7d' or '30d', and mirrors REST /usage. This goes beyond annotations without contradicting them, though it doesn't detail response structure or error cases.
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 four sentences, each serving a purpose: what it does, when to use it, an alternative, and a compatibility note. It is front-loaded with the core function and avoids fluff, making it easy to parse.
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 tool with only one optional parameter and no output schema, the description is highly complete. It covers the data returned, the supported periods, the use case, privacy, and relationship to REST, giving the agent enough context to invoke it correctly without ambiguity.
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 0%, so the description compensates by explicitly naming the period parameter and its allowed values ('7d' or '30d'). This gives the agent clarity on what to pass, which the schema alone lacks (only a default without enum or description).
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 identifies the tool's purpose: returning the user's own API usage, including total calls, per-day series, and top endpoints. It distinguishes itself from sibling tools by emphasizing 'Your own API usage' and 'Private to your account', making it unique among the many get_* data tools.
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 states when to use the tool: 'Use it to budget calls' and provides a direct alternative for related functionality: 'get_check_history shows the remaining count'. Also references REST /usage for developers familiar with the API, giving clear contextual placement.
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 distinct purposes, but a few pairs like get_liquidation_map vs get_liquidity_map and multiple signal-fetching tools (get_signal, get_signals_since, list_signals, get_actionable_signals) could be confused without careful description reading. Overall, each tool has a clearly defined role, so the ambiguity is limited.
The naming is overwhelmingly consistent: snake_case verb_noun patterns like get_price, list_signals, close_trade, and amend_trade. The exception is 'health' as a plain noun, and a few compound verbs like find_similar_signals, but the overall pattern is strictly followed.
With 52 tools, this is far beyond the typical well-scoped range and falls into the 'too many' category. The broad platform scope justifies many tools, but the sheer number makes the set heavy and harder for an agent to navigate efficiently.
The surface covers the full lifecycle: journal management (log, amend, close, void, read), trade vetting (check, plan, rank), signal discovery (list, get, actionable, similar), market data (price, levels, indicators, options), context (macro, liquidity, flows), forensics (manipulation, trust, narrative), and meta (playbook, usage, health). No obvious gaps or dead ends.