charm
Server Details
OEM automotive repair data - part numbers, torque specs, procedures for 1982-2013 vehicles.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
The two tools have completely distinct purposes: one lists available manufacturers, the other performs lookups. There is no ambiguity or overlap.
Both tools share the 'charm_' prefix, but 'charm_list_makes' follows a verb_noun pattern while 'charm_lookup' is a single verb. This minor inconsistency is acceptable.
With only 2 tools, the server is minimal but covers the essential workflow: checking coverage and querying data. A slightly larger set could be justified, but this is reasonable.
The set covers listing all makes and performing lookups with various parameters. No critical gaps are apparent for the intended domain of vehicle specifications.
Available Tools
2 toolscharm_list_makesAInspect
List all vehicle manufacturers covered by CHARM (1982-2013). Use to check coverage before a lookup.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the tool lists all manufacturers but does not describe output format, limitations, or any behavioral traits. The output schema exists, partially compensating, but the description could add context like 'returns an array of strings'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words, front-loaded with the main action. Efficient and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple parameterless tool, the description adequately covers purpose and usage. Output schema provides the return structure. Completeness is high given the low complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema coverage is 100% with nothing to add. Baseline for 0 parameters is 4, and the description adds no parameter information, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists all vehicle manufacturers covered by CHARM with a specific year range. It distinguishes from the sibling tool charm_lookup by indicating this is for checking coverage before a lookup, making purpose very specific and clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'Use to check coverage before a lookup,' which provides clear guidance on when to use this tool versus the sibling charm_lookup. This is a strong example of usage guidelines.
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")
| Name | Required | Description | Default |
|---|---|---|---|
| make | No | ||
| year | No | ||
| model | No | ||
| query | Yes | ||
| system | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description explicitly states data is sourced from factory service manuals (not model-generated), adding transparency. With no annotations provided, the description adequately informs about data authenticity and structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise with two paragraphs and a list, no redundant information. Front-loaded with core purpose and key usage advice.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers tool purpose, input guidance, and data source. Output schema exists, so return format details are unnecessary. Sibling tool is listed but not further contextualized, leaving minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema coverage, the description explains each parameter in the Args section with examples (e.g., 'front brake caliper part number'), adding significant meaning beyond the schema's titles and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool looks up verified OEM part numbers, torque specs, fluid capacities, and service procedures for vehicles 1982-2013. It distinguishes itself from sibling tool 'charm_list_makes' by focusing on detailed vehicle data retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description advises providing year+make+model for precise answers, giving clear usage context. It doesn't explicitly mention when not to use, but the sibling tool listing implies alternatives for listing makes.
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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