Skip to main content
Glama

LLM Provider Intelligence

by org.memoryapi

recommend_llm

Get the best LLM provider for your specific agent task. Returns top 3 ranked recommendations with reasoning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskNoTask type: tool_calling, reasoning, rag, summarization, high_volume, low_latency, generalgeneral
priorityNoOptimize for: balanced, cost, speed, qualitybalanced
min_contextNoMinimum context window needed in tokens
require_tool_callingNoOnly return providers with tool calling support
max_price_per_millionNoMaximum input price per million tokens (USD)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It states returns top 3 ranked recommendations with reasoning, but doesn't disclose side effects, dependency on live data, or any limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, clear and to the point. Could be slightly more detailed without becoming verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter recommendation tool with no output schema or annotations, the description is adequate but lacks depth about how recommendations are generated or caveats.

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 covers all 5 parameters with descriptions (100% coverage). Tool description adds minimal extra meaning beyond schema, only mentioning output format.

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?

Description clearly states verb 'Get' and specific resource 'best LLM provider for your specific agent task'. Distinguishes from siblings check_provider_status and list_providers.

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?

Implies use for getting task-specific recommendations. No explicit when-not or alternatives, but context with siblings provides some guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All LLM Provider Intelligence toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct, non-overlapping purpose: checking operational status, listing providers with detailed info, and recommending based on task. No ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with clear verbs (check, list, recommend) and nouns (provider_status, providers, llm).

Tool Count5/5

Three tools is well-scoped for the server's purpose—covering status check, listing, and recommendation without being too few or excessive.

Completeness4/5

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.

Resources