Fetch request and token usage totals for the caller API key. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | Optional LMX API key (lmx_...). Prefer MCP client Authorization header or env; use this to override per call. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It mentions 'Requires authentication' but does not disclose if it is read-only or any potential side effects. For a simple fetch, it is adequate but could be improved.
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 sentences, no fluff, front-loaded with the core action. Every word 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 simple fetch tool with no output schema, the description is minimally sufficient. However, it could briefly mention what is returned (e.g., totals object) to improve completeness.
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 single parameter 'api_key' has a schema description already, but the tool description adds useful context on when to use it (override per call) and precedence over client headers/env. This adds value beyond the schema.
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 action ('Fetch') and resource ('request and token usage totals for the caller API key'), which is distinct from sibling tools like chat_completion or get_balance.
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?
No explicit guidance on when to use this tool versus alternatives. While authentication is noted, there is no mention of when to prefer this over get_pricing or get_balance for usage-related queries.
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 has a clearly distinct purpose: chat completion, balance, pricing, status, usage, models, cost estimation, and web search. No two tools perform overlapping functions.
All tools follow a consistent snake_case verb_noun pattern (e.g., chat_completion, get_balance, list_models). No mixing of conventions.
8 tools is well-scoped for a cloud LLM inference server, covering core operations (chat, models, pricing, usage, balance, status) plus a web search add-on. Not excessive or too sparse.
The tool set covers essential LLM inference and account management workflows. Minor gaps exist (e.g., no streaming parameter docs, no model detail retrieval), but the core surface is complete.