List LLM providers with their capabilities, pricing, and agent scores. Filter by specific requirements.
| Name | Required | Description | Default |
|---|---|---|---|
| best_for | No | Filter by use case: tool_calling, reasoning, cost, speed, long_context, rag | |
| max_price | No | Max input price per million tokens | |
| x402_native | No | Only show x402-native providers | |
| tool_calling | No | Only show providers with tool calling |
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?
As a listing tool, it is implicitly read-only, but the description does not explicitly state safety, side effects, or performance characteristics. With no annotations provided, the description carries the full burden but only describes what it returns, not behavioral traits.
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 with no wasted words. The first sentence captures core purpose, the second adds filtering capability. Efficient and front-loaded.
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?
The description covers purpose and filtering, but omits output format or error behavior. Given no output schema, some details on return structure could improve completeness, but it remains adequate for a simple listing tool.
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 coverage is 100%, so the description adds little meaning beyond the schema. The filter statement is generic; no additional context for enums or parameter usage patterns.
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 lists LLM providers with capabilities, pricing, and agent scores, and supports filtering. This distinguishes it from sibling tools like check_provider_status and recommend_llm.
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 over alternatives. The description only says 'filter by specific requirements' without indicating prerequisites or scenarios where other tools are more appropriate.
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 distinct, non-overlapping purpose: checking operational status, listing providers with detailed info, and recommending based on task. No ambiguity.
All tool names follow a consistent verb_noun pattern with clear verbs (check, list, recommend) and nouns (provider_status, providers, llm).
Three tools is well-scoped for the server's purpose—covering status check, listing, and recommendation without being too few or excessive.
The tool surface covers the core workflows (status, info, recommendation). A minor gap is direct provider comparison, but list_providers with filtering and recommend_llm adequately address it.