charm

charm_lookup

Look up verified OEM part numbers, torque specs, fluid capacities, and service procedures for vehicles 1982-2013.

Returns structured data sourced directly from factory service manuals (not model-generated).
Provide year+make+model when known for a precise, isolated answer.

Args:
    query: What you want to know (e.g. "front brake caliper part number", "spark plug gap", "engine oil capacity")
    year: 4-digit year, 1982-2013
    make: Manufacturer (e.g. "Ford", "Honda", "Chevy Truck")
    model: Model name (e.g. "Taurus X", "Civic", "Silverado")
    system: Optional system filter (e.g. "brakes", "engine", "electrical")

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "charm_lookup"
  }
}

Input Schema

Name	Required	Description	Default
`make`	No
`year`	No
`model`	No
`query`	Yes
`system`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries the transparency burden. It discloses that results are 'sourced directly from factory service manuals (not model-generated)' and implies a read-only operation. However, it does not address potential error conditions, rate limits, or what happens with incomplete vehicle information.

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

Conciseness5/5

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

The description is well-structured: a one-sentence purpose, a source note, a usage tip, and a clear Args list. Every sentence adds value and there is no fluff or redundancy.

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?

The description adequately covers the 5 parameters (1 required) and provides usage context. Since an output schema exists, return value explanations are not necessary. A minor gap is the lack of detail about how the tool behaves when only the query is provided without vehicle info.

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?

The input schema has no descriptions (0% coverage), but the description's 'Args:' section fully explains each parameter with type, optionality, and examples. For instance, year is '4-digit year, 1982-2013' and system is 'Optional system filter (e.g. 'brakes', 'engine', 'electrical')'.

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 the tool's function with a specific verb and resource: 'Look up verified OEM part numbers, torque specs, fluid capacities, and service procedures for vehicles 1982-2013.' This directly distinguishes it from the sibling tool charm_list_makes, which is about listing makes, not providing detailed specifications.

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?

The description provides clear usage context: 'Provide year+make+model when known for a precise, isolated answer.' This indicates when the tool should be used and how to get better results, though it does not explicitly mention alternatives or exclusions.

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

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