get_pricing
Fetch current LMX Cloud per-call pricing catalog.
Input Schema
TableJSON Schema
| 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. |
LMX Cloud LLM Inference
by io.lmxcloud
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; the description merely says 'Fetch current ... pricing catalog', which indicates a read operation but doesn't cover authentication needs, caching, or other behavioral traits. It adds minimal context beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence that conveys the core action without any waste. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and no output schema, the description adequately conveys the purpose. However, it does not describe the return format, which could be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the description adds no meaning beyond what the schema provides for the api_key parameter. The description focuses on purpose, not parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch', the resource 'pricing catalog', and specifies it's for 'LMX Cloud per-call', which distinguishes it from siblings like get_balance or get_usage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use when pricing information is needed, with no explicit exclusions or alternatives. The name itself clearly indicates its purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A4/5.0
Disambiguation5/5
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.
Naming Consistency5/5
All tools follow a consistent snake_case verb_noun pattern (e.g., chat_completion, get_balance, list_models). No mixing of conventions.
Tool Count5/5
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.
Completeness4/5
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.