A
licenseA
qualityC
maintenanceProvides vehicle wheel and tyre fitment data for 10,000+ makes, models, and trim levels through 32 read-only tools, enabling searches by vehicle, rim, or tire specifications.
32
75
MIT
charm
Server Details
OEM automotive repair data - part numbers, torque specs, procedures for 1982-2013 vehicles.
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- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- bitMacrocode/charm-mcp
- GitHub Stars
- 0
- Server Listing
- CHARM
Available Tools
2 toolscharm_list_makesAInspect
List all vehicle manufacturers covered by CHARM (1982-2013). Use to check coverage before a lookup.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
A4.2/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clearly indicates a read-only listing operation and its scope, but it does not disclose return format, pagination, or potential errors. It is not misleading, but additional behavioral details would improve transparency.
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?
Two tightly worded sentences, front-loaded with the action, and no wasted words. The practical usage tip adds value without clutter.
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?
For a simple zero-parameter list tool with an output schema, the description fully explains what it does and when to use it. The timeframe and purpose are stated, and the output schema covers return values, so nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema fully covers parameter semantics. The description adds nothing about parameters, but none are needed, which matches the baseline for a no-parameter tool.
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?
Description clearly states the tool's action (list) and resource (vehicle manufacturers), with specific scope (CHARM 1982-2013). It implicitly distinguishes from the sibling tool charm_lookup by serving as a coverage check before lookups.
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 explicitly says 'Use to check coverage before a lookup,' providing clear context and timing. It implies the sibling tool but does not explicitly name it or state exclusions, so not a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
charm_lookupAInspect
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")
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| year | No | ||
| model | No | ||
| query | Yes | ||
| system | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
charm_list_makes - First observed
charm_lookup
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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.