Skip to main content
Glama

configure_brain

Set the default or session-specific AI model for routing LLM requests across multiple providers. Configure provider, API key, model parameters, and system prompt to tailor responses.

Instructions

Configure the default or session-specific brain model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNo
base_urlNo
providerNo
timeout_sNo
extra_bodyNo
max_tokensNo
session_idNo
api_key_envNo
temperatureNo
extra_headersNo
system_promptNo
reasoning_effortNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It says nothing about side effects, persistence, what 'default' vs 'session-specific' means, whether changes are reversible, or any required authentication or context. This is a significant gap for a configuration tool.

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

Conciseness2/5

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

The description is a single clear sentence, but it is under-specified for a tool with 12 optional parameters. Conciseness is not merely brevity; here it omits essential information, making it more of an under-specification than an efficient summary.

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

Completeness1/5

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

Given the tool's complexity (12 parameters, no annotations, no output schema provided) the description is wholly inadequate. It does not explain how to select default vs session-specific scope, what parameters do, or what the response will look like. The presence of an output schema partially covers return values, but all other context is missing.

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

Parameters1/5

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

Schema description coverage is 0%, so the description must compensate by explaining parameters. It does not mention any of the 12 parameters (e.g., model, base_url, session_id), leaving their meanings entirely undisclosed beyond the schema's variable names and defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action 'Configure' and the resource 'brain model', with a distinction between 'default' and 'session-specific'. This is specific enough to identify the tool's core function, though it does not explicitly differentiate from sibling tools like get_brain_config.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives, nor any mention of prerequisites or context. The description simply states what the tool does without indicating the appropriate scenarios, making it insufficient for an agent to choose between this and related tools like start_session or configure_memory.

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/groxaxo/mcp-llm-router'

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