Skip to main content
Glama

llm_auto

Routes AI tasks to the best model while tracking savings server-side, useful for hosts without a UserPromptSubmit hook.

Instructions

Auto-routing wrapper with persistent savings tracking — works from any host.

Equivalent to llm_route but additionally:
- Flushes pending hook-written savings records into SQLite before routing.
- Appends a compact savings envelope every 5 calls so you can see the
  cumulative value across all sessions and hosts without running llm_savings.

Use llm_auto instead of llm_route when you are in a host that lacks a
UserPromptSubmit hook (Codex CLI, Claude Desktop, GitHub Copilot) — the
savings are tracked server-side, so they accumulate correctly regardless of
which client triggered the call.

Args:
    prompt: The task or question to route.
    task_type: Optional hint — "query", "research", "generate", "analyze", "code".
    profile_override: Force a routing profile — "budget", "balanced", or "premium".
    system_prompt: Optional system instructions.
    context: Optional conversation context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
task_typeNo
profile_overrideNo
system_promptNo
contextNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool flushes pending savings records into SQLite before routing and appends a savings envelope every 5 calls. It also notes server-side tracking. However, it does not mention potential side effects, error behaviors, or limitations beyond this, so a small gap exists.

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?

The description is concise and well-structured, with a header, a usage paragraph, and a bulleted args list. It slightly repeats 'from any host' and 'across all sessions', but overall it is efficient and front-loaded.

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?

Given the existence of an output schema and the presence of many sibling tools, the description provides sufficient context for correct selection and invocation. It explains the core purpose, usage guidelines, and parameter meanings. It does not cover error handling or detailed behavior, but the output schema likely handles return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates by providing meaningful descriptions for all 5 parameters. It adds enumerated values for 'task_type' and 'profile_override', and clarifies the purpose of each parameter beyond their types.

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 it is an auto-routing wrapper with persistent savings tracking, and explicitly distinguishes it from sibling llm_route by listing additional features. The purpose is specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Use llm_auto instead of llm_route when you are in a host that lacks a UserPromptSubmit hook'. It provides concrete examples (Codex CLI, Claude Desktop, GitHub Copilot) and explains the benefit.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ypollak2/llm-router'

If you have feedback or need assistance with the MCP directory API, please join our Discord server