Car repair cost estimates by make, model and repair type. 31 cost guides, VIN decode. Free.
Decode a 17-character Vehicle Identification Number (VIN) into year, make, model, trim, body style, and manufacturer. Use before estimate_vehicle_value to get exact vehicle details.
| Name | Required | Description | Default |
|---|---|---|---|
| vin | Yes | 17-character VIN (letters I, O, Q are not used) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful context about the decoded fields but does not disclose edge-case behavior such as how invalid VINs are handled or whether partial matches are returned.
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 with no filler. The core function is stated first, followed by a useful usage pointer. Every word earns its place.
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 single-parameter, read-only decoding tool, the description covers what the tool does, what it returns, and how it fits into the larger workflow with estimate_vehicle_value. No critical information an agent needs before calling it is missing.
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?
Schema description coverage is 100%, including a pattern and length constraints for the vin parameter. The description restates '17-character' but does not need to add much more because the schema already defines the parameter clearly.
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 states a specific verb ('Decode'), a specific resource (17-character VIN), and the exact outputs (year, make, model, trim, body style, manufacturer). It also distinguishes itself from estimate_vehicle_value by identifying itself as the prerequisite step.
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?
The description explicitly tells the agent to use this tool before estimate_vehicle_value, giving clear contextual guidance. It does not enumerate when not to use it or name other alternatives, but the workflow hint is specific and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate the current US market value (USD) of a vehicle from year, make, model, and optional trim. Deterministic model: base price by make, trim multiplier, age-based depreciation. Useful to judge whether a repair is worth the cost.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | Make name, e.g. "Toyota", "BMW", "Ford" | |
| trim | No | Optional trim, e.g. "Limited", "XLT", "Sport" | |
| year | Yes | Model year, e.g. 2019 | |
| model | Yes | Model name, e.g. "Camry", "F-150" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and non-destructive. The description adds meaningful behavioral detail beyond that: the valuation is deterministic and uses base price by make, trim multiplier, and age-based depreciation. This helps the agent set expectations about consistency and methodology.
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?
The description is two sentences with no filler. The core purpose is front-loaded, the input list is compact, and the methodology and use case are conveyed efficiently in the remaining sentences.
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 deterministic valuation tool, the description covers inputs, output currency/value, methodology, and intended use case. There is no output schema, so the description does adequately indicate the result is a USD market value, although it does not describe error behavior for unsupported makes/models.
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?
Schema description coverage is 100%, so the schema already documents each parameter with examples. The description adds a little context by noting trim is optional and contributing via a 'trim multiplier', but the schema carries most of the semantic weight, so the baseline of 3 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 states a specific verb and resource: 'Estimate the current US market value (USD) of a vehicle'. It also names the exact inputs (year, make, model, optional trim), which distinguishes it clearly from the repair-cost and vehicle-lookup sibling tools.
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?
The description provides a clear use context: 'Useful to judge whether a repair is worth the cost.' This implies when an agent should call it relative to repair-cost tools, though it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Estimate the cost of a car repair job (parts + labor, USD, US national averages 2026) by make and repair type. Returns low/high ranges for parts, labor, and total. Use list_makes_models and list_repair_types to discover valid slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| make_slug | Yes | Car make slug, e.g. "ford", "bmw", "tesla" | |
| part_slug | Yes | Repair type slug, e.g. "brake-pads", "head-gasket", "windshield" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context: it returns low/high ranges for parts, labor, and total, and specifies the data basis as US national averages for 2026. This goes beyond the annotations without contradicting them.
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?
The description is compact: two sentences that front-load the core purpose, then state the return format, then provide prerequisite lookup guidance. Every sentence earns its place with no repetition or filler.
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?
The description explains what the tool returns, the currency and region basis, and how to obtain valid parameters, which is sufficient for a read-only estimate tool with no output schema. It stops short of covering possible error behavior or clarifying the relationship to get_repair_cost_guide, but these are minor gaps.
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?
Schema description coverage is 100%, and both make_slug and part_slug are documented with examples. The description reinforces that part_slug is a repair type and tells the agent to discover valid slugs, but adds little semantic meaning beyond what the schema already provides. Baseline 3 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 estimates car repair costs by make and repair type, with parts, labor, and total ranges in USD. It is specific about the resource and action, but it does not explicitly differentiate itself from the sibling tool get_repair_cost_guide, which could plausibly cover similar territory.
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?
The description provides explicit guidance to use list_makes_models and list_repair_types to discover valid slugs, which is practical for invocation. However, it does not state when to prefer this tool over get_repair_cost_guide or other sibling tools, so the when-to-use guidance is only partially addressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get the full editorial cost guide for a repair type: direct answer, price range, cost breakdown table, price factors, failure symptoms, and FAQs (US market, 2026). Use list_repair_guides to discover slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Guide slug, e.g. "head-gasket", "transmission", "windshield" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by specifying the exact data returned (price range, breakdown table, factors, symptoms, FAQs) and scoping it to the US market in 2026, which is meaningful beyond the annotation.
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?
A single sentence that front-loads the primary purpose and deliverable contents, then closes with a concise routing instruction. Every word earns its place with no repetition or filler.
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?
With only one parameter, no nested objects, and no output schema, the description adequately conveys what the tool returns and how to obtain a valid input. The enumerated content sections give the agent a clear picture of the response without needing an output schema.
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?
Schema coverage is 100%, so the schema already documents the slug parameter with examples. The description adds value by telling the agent where to find valid slug values (list_repair_guides), which is actionable guidance the schema does not provide.
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?
States a specific verb ('Get') and resource ('full editorial cost guide for a repair type'), then enumerates the concrete contents: direct answer, price range, cost breakdown table, price factors, failure symptoms, and FAQs. The 'full editorial cost guide' phrasing distinguishes it from the sibling get_repair_cost_estimate, so the agent can tell them apart.
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 instructs the agent to use list_repair_guides to discover slugs, which is a clear prerequisite. It does not explicitly state when to prefer get_repair_cost_estimate instead, but the 'full editorial cost guide' framing makes the distinction reasonably clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all supported car makes and their models (slugs, EV flag, price multiplier). Use the make_slug values with get_repair_cost_estimate and list_repair_types.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and non-destructive. The description adds useful behavioral context beyond the annotations by specifying the contents of the catalog (slugs, EV flag, price multiplier) and confirming it returns the full supported set rather than a filtered subset.
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 with no redundant wording. The first sentence states scope and return fields; the second provides actionable downstream guidance. Every clause earns its place.
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 parameterless read-only list tool with no output schema, the description is complete: it states the exhaustive scope, lists the relevant output fields, and explains how to consume the results with sibling tools. Nothing needed for correct invocation is missing.
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 schema has zero parameters, so there is nothing for the description to explain about parameter meaning. The description still adds value by clarifying what the returned data contains and how the make_slug values should be used downstream.
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 uses a specific verb ('List') with a clearly bounded resource ('all supported car makes and their models') and names the exact fields returned. This distinguishes it from other list-style siblings like list_repair_types and list_repair_guides by focusing on the vehicle make/model catalog.
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?
The description tells the agent exactly what to do with the output: use make_slug values with get_repair_cost_estimate and list_repair_types. For a parameterless catalog tool this is the primary usage, so no alternative-routing guidance is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all editorial repair cost guides (slug, title, category, price range). Optionally filter by category: body, mechanical, glass-wheels, or electrical.
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | Optional guide category filter |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds context about the returned fields and the optional category filter, but does not disclose pagination, ordering, or any quirks about the 'all' semantics. This is acceptable but does not go beyond what a standard read-only list tool implies.
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?
A single, well-structured sentence that front-loads the action and resource, lists the returned fields, and then states the optional filter. Every clause earns its place with no redundancy or filler.
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, read-only list tool with one optional enum parameter and no output schema, the description fully covers what the agent needs: the returned fields, the filter options, and the safety context from annotations. There are no significant missing details that would prevent correct invocation.
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?
Schema description coverage is 100%, and the single parameter 'category' is already fully documented in the schema with its enum values. The description repeats the same enum options without adding new semantic meaning, so it earns the baseline score of 3.
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 states a specific verb ('List'), a clear resource ('editorial repair cost guides'), and the returned fields (slug, title, category, price range). It also distinguishes itself from the singular sibling get_repair_cost_guide and from list_repair_types by narrowing to repair cost guides with optional category filtering.
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?
The description clearly establishes when to use this tool: to list all editorial repair cost guides, optionally filtered by category. It provides the exact valid filter values, which gives an agent actionable context. However, it does not explicitly state when NOT to use it or name alternatives such as get_repair_cost_guide for retrieving a single guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the repair job types available for cost estimates (slugs, typical parts cost, labor hours). Pass a make_slug to get only the jobs applicable to that make (EVs skip ICE-only jobs).
| Name | Required | Description | Default |
|---|---|---|---|
| make_slug | No | Optional car make slug to filter jobs applicable to that make |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond that: it returns slugs, typical parts cost, and labor hours, and that EVs skip ICE-only jobs. No contradiction with annotations.
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 with no filler. The core purpose is front-loaded, and the optional-parameter guidance is concise and directly useful.
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 optional-parameter read-only list tool, the description covers what the tool returns, how filtering works, and the EV behavior. No output schema is present, but the description explains the return contents sufficiently.
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?
Schema coverage is 100% and the schema describes make_slug as an optional filter. The description goes further by explaining the practical effect ('EVs skip ICE-only jobs') and the output fields, adding value beyond the schema alone.
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 states a specific verb (List), a clear resource (repair job types), and their purpose (cost estimates: slugs, typical parts cost, labor hours). This distinguishes it from siblings like list_repair_guides and get_repair_cost_estimate, so an agent can tell when this tool is the right choice.
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?
The description clearly explains the optional make_slug filtering behavior and the EV-specific nuance, which gives context for when to use the parameter. It does not explicitly name alternatives or exclusion conditions, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
decode_vinestimate_vehicle_valueget_repair_cost_estimateget_repair_cost_guidelist_makes_modelslist_repair_guideslist_repair_typesClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
Auto repair price benchmarks and current service deals for any US ZIP. No account required.
Decode VINs, search recalls, complaints, crash ratings, and investigations.
Decode any VIN and check open NHTSA safety recalls. Free official US government data, no auth.
UK car repair and servicing cost ranges per car model, in GBP, from PriceMyRepair.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource or action: VIN decoding, vehicle valuation, repair cost estimates, editorial guides, and discovery lists are clearly separated. get_repair_cost_estimate and get_repair_cost_guide are slightly overlapping since both deal with repair pricing, but their descriptions clarify the estimate-vs-editorial-guide distinction.
All tool names follow a consistent verb_noun snake_case pattern: decode_vin, estimate_vehicle_value, get_repair_cost_estimate, get_repair_cost_guide, list_makes_models, list_repair_guides, list_repair_types. The naming is predictable and groups related operations clearly.
Seven tools is well-scoped for a car repair cost estimation server. Each tool earns its place: three discovery tools, two repair-cost tools, one VIN decoder, and one vehicle value estimator cover the domain without unnecessary bloat.
The tool surface covers the core workflows: discovering supported makes/models and repair types, decoding VINs, estimating vehicle value, getting repair cost estimates, and retrieving detailed editorial guides. There are no obvious dead ends, and the domain is read-only so CRUD-style gaps do not apply.
