Skip to main content
Glama

vehicle_control

Apply clamped vehicle inputs such as throttle, brake, steering, gear, and clutch, using ADAS-safe arbitration by default or explicit direct control for isolated automation sessions.

Instructions

Apply clamped vehicle inputs with ADAS-safe or explicit direct arbitration.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commandYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
dataNo
messageYes
Behavior2/5

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

Description mentions 'clamped' and arbitration modes, which add context beyond annotations (all false). But it does not disclose behavioral traits such as whether inputs are applied immediately, what happens if vehicle_id is invalid, or if changes persist after session ends. With no annotations to rely on, this is insufficient.

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

Conciseness3/5

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

Single sentence, very concise. However, it sacrifices completeness and clarity. Front-loads the main idea but lacks supporting details that would earn its place.

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

Completeness2/5

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

Given the complexity (9 subparameters) and the presence of an output schema, the description is too sparse. It does not mention the required vehicle_id, nor does it describe the return value or error conditions. Incomplete for an agent to use effectively without additional context.

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

Parameters2/5

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

Schema description coverage is 0%, meaning the description offers no explanation of the 'command' parameter or its properties. While the schema itself has descriptions for subfields, the tool description should help an agent understand what the object contains. It fails to do so, leaving the agent to rely solely on the schema.

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?

Description clearly states it applies clamped vehicle inputs, specifying two arbitration modes (ADAS-safe or direct). Verb 'Apply' and resource 'vehicle inputs' are specific. However, 'clamped' and 'ADAS-safe' are jargon that may not be universally understood. It distinguishes from sibling tools like vehicle_ai_configure or vehicle_state.

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

Usage Guidelines1/5

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

No guidance on when to use this tool versus alternatives. Does not explain when to choose ADAS-safe versus direct arbitration, nor mention prerequisites like requiring a spawned vehicle. Lacks any when-not-to-use or exclusion criteria.

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/eric-rolph/beamng-mcp'

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