Fetch LMX Cloud provider health, fallback chain, and anchoring status.
| 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 full burden for behavioral disclosure. It mentions what is fetched (health, fallback chain, anchoring status) but omits details on authentication behavior (e.g., effect of missing api_key), rate limits, or side effects. This is a significant gap for a tool that may require authorization.
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 sentence that efficiently conveys the core purpose. Every word is necessary and earns its place. There is no redundancy or superfluous information.
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?
No output schema is provided, and the description does not specify the response format or any details about the returned data. Given the complexity of 'fallback chain' and 'anchoring status', the description is inadequate for an agent to fully understand what to expect.
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 description coverage is 100% for the single parameter (api_key), and the schema includes a detailed description. The tool description does not add extra meaning beyond the schema, so baseline score 3 is appropriate.
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 fetches 'LMX Cloud provider health, fallback chain, and anchoring status'. It uses a specific verb ('Fetch') and specifies multiple distinct aspects of status, distinguishing it 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?
The description implies usage for checking status but provides no explicit guidance on when to use vs. alternatives, nor any exclusions or when-not-to-use scenarios. It lacks context about prerequisites or prioritization among siblings.
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.