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Glama

car_info

Identifies the car database used by Penelope MCP tools and lists its table names. Query by car name to get database details for telemetry analysis.

Instructions

Which car's database these tools read, and its table names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
carNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

D1.5/5.0
Behavior1/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention side effects, permissions, data scope, or return format. It only vaguely states the tool reads 'which car's database' — unclear if this is a query or a metadata lookup. Absolutely no behavioral transparency.

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 extremely short (one clause), so it is concise in length, but this is under-specification rather than good conciseness. It fails to convey the tool's purpose or usage. Front-loading is irrelevant when the content is nearly meaningless.

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?

For a low-complexity tool with one optional parameter and an output schema, the description still must clarify what the tool returns. It vaguely mentions 'database these tools read' and 'table names,' but doesn't explain the output structure or how it relates to the parameter. The output schema might help, but the description alone is insufficient for correct invocation.

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?

The schema has one parameter 'car' with no description (0% coverage). The description does not add any meaning about the parameter's type, allowed values, or its effect on the output. The agent gets zero help understanding what to pass for 'car'.

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

Purpose2/5

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

The description is a noun phrase ('Which car's database these tools read, and its table names') rather than a clear verb+resource statement. It hints at the tool providing database/schema context, but doesn't explicitly state what action the tool performs or what the returned data represents. This is more of a label than a purpose statement.

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?

There is no guidance on when to use this tool vs the siblings (export_query, get_series, etc.). No mention of prerequisites, conditions, or alternatives. The agent is left entirely without direction on selecting this tool.

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