FREE: your recent billed calls (line-item ledger with request_id, endpoint, address, cost) so you can reconcile spend against reports.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | max rows, default 100, max 1000 |
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | ||
| items | No | ||
| spent_today_usd | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations by noting the tool is FREE and returns line-item details. Annotations already declare readOnlyHint=true, so no contradiction. The description enhances understanding of what the tool does.
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 a single, well-structured sentence that conveys all necessary information without redundancy or 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?
Given the presence of an output schema and the tool's simplicity (read-only list with one parameter), the description is adequately complete. It mentions the key output fields, providing sufficient context for an AI agent.
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 input schema already provides full coverage for the single parameter 'limit', describing default and max. The description does not add additional semantic meaning beyond what the schema provides.
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 recent billed calls with specific fields (request_id, endpoint, address, cost). It uses specific verbs and resources, and distinguishes itself from siblings by focusing on usage reconciliation.
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 explicitly mentions the use case ('so you can reconcile spend against reports'), providing clear context. However, it does not explicitly exclude scenarios or mention alternative tools.
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: account management, ground motion queries, batch processing, history, and Q&A. No two tools serve overlapping purposes, and descriptions clearly differentiate them.
Most tools use a 'verb_noun' pattern with verbs like get, register, buy, check, ask. However, 'check' is used for two tools (check_ground_motion, check_portfolio) while similar retrieval tools use 'get' (get_motion_history), creating a slight inconsistency in verb choice.
With 9 tools, the server covers account setup, credit management, ground motion queries (single, batch, history), and follow-up Q&A—a well-scoped set that earns each tool's place.
The tool surface covers the full lifecycle: account creation (register_agent), credit purchasing (buy_credits), balance/usage (get_account, get_usage), coverage checks (get_coverage), ground motion queries (check_ground_motion, check_portfolio), motion history (get_motion_history), and post-purchase Q&A (ask_inspector). No obvious gaps.
