Real-time web search via LMX (Brave Search passthrough). Fixed per-call USDC price from the caller's API key balance. Returns title/url/snippet results.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query string. | |
| api_key | No | Optional LMX API key (lmx_...). Prefer MCP client Authorization header or env; use this to override per call. | |
| max_results | No | Max results to return (1–20, default 5). |
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?
No annotations provided, so description carries full burden. It discloses real-time search, LMX passthrough, fixed per-call USDC pricing, and return format (title/url/snippet). However, it does not mention rate limits, error handling, or query modification. A 3 is appropriate as it covers key behaviors but lacks depth.
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?
Three short, front-loaded sentences that convey purpose, cost, and output. No filler or redundant information. Every sentence adds value, making it highly efficient.
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 search tool with no output schema, the description explains purpose, cost, and return fields. It lacks mention of pagination, result count behavior (though max_results param exists), and error handling. Still, it covers the essential context for an agent to use it correctly.
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%, so baseline is 3. The tool description adds no additional parameter meaning beyond what the schema already provides (e.g., query, api_key, max_results). Thus, it meets the minimum but does not enhance parameter understanding.
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?
Clearly states 'Real-time web search' via LMX (Brave Search passthrough), which is distinct from sibling tools like chat_completion or get_balance. The verb 'search' and resource 'web' are specific, and the tool is unambiguously differentiated.
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?
Implies use for real-time web search queries. While no explicit alternatives or exclusions are given, the sibling tools are all unrelated (balance, pricing, models), so the context is clear. A brief 'when to use' would improve, but not necessary given differentiation.
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.