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charm

by io.github.bitMacrocode

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")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeNo
yearNo
modelNo
queryYes
systemNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

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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TDQS

A4.2/5.0
Disambiguation5/5

The two tools have clearly distinct roles: one checks coverage by listing manufacturers, the other retrieves specific vehicle data. There is no overlap in functionality, so an agent can easily choose between them.

Naming Consistency4/5

Both tools share the 'charm_' prefix, and each name starts with a verb ('list' and 'lookup'). However, 'charm_list_makes' follows a verb-noun pattern while 'charm_lookup' is a bare verb, creating slight asymmetry.

Tool Count3/5

With only two tools, the server feels thin for the breadth of data it claims to cover (vehicles 1982-2013). While the two tools form a minimal workflow, the count is borderline acceptable for a simple read-only API.

Completeness4/5

The tool set covers the core workflow of checking coverage and performing lookups. A notable gap is the absence of a way to list models or years for a given make, but an agent can work around this by attempting a lookup.

Resources