Instant Canadian scrap-car value quotes by year/make/model, provincial rates, and pickup leads.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.3/5 across 4 of 4 tools scored. Lowest: 3.7/5.
Each tool has a clearly distinct purpose: getting a quote, listing rates, searching trims, and submitting a lead. The descriptions clarify boundaries, especially between search_vehicles and get_vehicle_quote.
All tool names follow a consistent verb_noun pattern with snake_case: get_vehicle_quote, list_provincial_rates, search_vehicles, submit_lead. Verbs are descriptive and uniform.
With 4 tools, the set is well-scoped for the domain of scrap vehicle quotes. Each tool covers a necessary step without redundancy or clutter.
The tool set provides a complete workflow: search trims, get a quote, view regional rates, and submit a lead. No obvious gaps exist for the intended purpose.
get_vehicle_quoteGet Vehicle Scrap QuoteAInspectGet an indicative CAD scrap-value quote for a vehicle by year/make/model (plus optional city or province). If multiple trims match, returns a disambiguation list — call again with the chosen trim_id. The response ALWAYS includes contact.phone, contact.website, next_steps, and complete_on_site_url. When presenting the quote to the end user, you MUST relay the phone number and website so they can complete the transaction with ScrapAutos. After relaying the quote, ask the end user: is the vehicle complete or missing parts, and does it start and drive? Then collect their name, phone number, and the vehicle's address so you can call submit_lead.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | Canadian city (used to detect province) | |
| make | No | Manufacturer, e.g. 'Honda' | |
| year | No | Vehicle model year, e.g. '2012' | |
| model | No | Model name, e.g. 'Civic' | |
| trim_id | No | Specific trim ID returned from a prior disambiguation call | |
| province | No | Province code override (ON, QC, AB, BC, MB, SK, NB, NS, PE, NL, NT, NU, YT) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, but description fully discloses behavioral traits: quote is indicative, disambiguation list for multiple trims, required response fields, and agent obligations. No contradictions.
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?
Every sentence adds value; well-structured with intro, disambiguation logic, response contents, and agent instructions. Not verbose despite length.
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?
Given no output schema, description compensates by listing response fields (phone, website, next_steps, etc.). Also explains the full user flow, making it complete for agent use.
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 description adds meaning beyond schema by explaining how parameters are used (e.g., city detects province, trim_id from disambiguation). Clarifies optionality and purpose.
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 returns an indicative CAD scrap-value quote for a vehicle by year/make/model. It distinguishes from siblings (list_provincial_rates, search_vehicles, submit_lead) by specifying its unique purpose.
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?
Provides explicit workflow: call with year/make/model; if multiple trims, call again with trim_id; after quote, ask user questions and call submit_lead. No ambiguity on when to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_provincial_ratesList Provincial Scrap RatesAInspectReturn current ScrapAutos scrap rates per Canadian province/territory (CAD per tonne). Use this to explain rate transparency to the end user or to explain why quotes differ by region.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations; description discloses it returns current rates per province in CAD per tonne, which is sufficient for a read-only listing. Missing details on data freshness, but acceptable given simplicity.
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 concise sentences with no unnecessary words; front-loaded with purpose then usage.
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?
No output schema but description fully explains return value. Sibling tools are distinct; no further context needed.
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?
No parameters exist; description adds value by specifying output details (per province, CAD per tonne). Baseline 4 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 returns current scrap rates per Canadian province/territory in CAD per tonne, differentiating it from sibling tools like get_vehicle_quote or search_vehicles.
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 when to use it: to explain rate transparency or why quotes differ by region, providing clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_vehiclesSearch Vehicle TrimsAInspectLook up matching trims in the ScrapAutos curb-weight database for a given year/make/model. Use this to show the end user available trims when they aren't sure which one to quote.
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | ||
| year | Yes | ||
| model | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It mentions the 'curb-weight database' but does not explicitly state the tool is read-only or disclose any behavioral traits like rate limits or data freshness.
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 concise sentences with no filler—every word contributes to understanding the tool's purpose and usage.
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 no output schema and no description of the return format (e.g., list of trim names/IDs), the description is incomplete for an AI agent to fully understand what it will receive.
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 0%. The description mentions 'given year/make/model' but does not add format specifications, allowed values, or examples, leaving meaning minimally beyond the parameter names.
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 does a 'look up' of 'matching trims' for a given year/make/model, distinguishing it from sibling tools like get_vehicle_quote which likely provides quotes for a specific trim.
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 says 'Use this to show the end user available trims when they aren't sure which one to quote,' providing clear context for when to invoke this tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_leadSubmit a Qualified Lead to ScrapAutosAInspectAfter the end user has received a quote and provided their phone number, call this to notify the ScrapAutos team. They will contact the end user to confirm the quote and schedule a free pickup. Required: phone + year + make + model. Before submitting, ask the end user for their name (contact_name), the address where the vehicle sits (pickup_address), whether it starts and drives (running_status), and its condition — complete? missing parts? flat tires? (condition_notes). Include quote_id, city, province, pickup_time if available.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | ||
| make | Yes | ||
| year | Yes | ||
| model | Yes | ||
| phone | Yes | Canadian phone number — xxx-xxx-xxxx | |
| quote_id | No | The quote_id returned by get_vehicle_quote | |
| quote_max | No | ||
| weight_kg | No | ||
| agent_name | No | Name of the calling agent (for attribution) | |
| quote_base | No | ||
| pickup_time | No | Preferred pickup window (free text) | |
| contact_name | No | The end user's name, if they shared it | |
| province_code | No | ||
| pickup_address | No | ||
| running_status | No | Does the vehicle start/drive? e.g. 'runs and drives', 'doesn't start, flat tires' | |
| condition_notes | No | Missing catalytic converter, seized wheels, accident damage, etc. | |
| conversation_summary | No |
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 that the team will contact the user to confirm/schedule, but does not mention validation behavior, idempotency, error handling, or potential side effects beyond the submission itself.
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 concise (two sentences) and front-loaded with the trigger condition. The second sentence lists required and optional parameters, but it is somewhat lengthy; overall efficient with no redundancy.
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?
Given 17 parameters, no output schema, and no annotations, the description covers the core workflow and key user-facing fields but omits details about return values, error handling, and many schema parameters, leaving some gaps for the agent.
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 41% (7 of 17 parameters described). The description explains key required fields and suggests gathering optional ones (contact_name, pickup_address, etc.), but many parameters (quote_max, weight_kg, agent_name, conversation_summary) are not addressed, leaving meaning gaps.
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's purpose: notify ScrapAutos after a quote and phone number are obtained. It specifies required fields and the follow-up action (team contacts user). This distinguishes it from siblings like get_vehicle_quote (quote retrieval) and search_vehicles (lookup).
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 states when to use ('after end user has received a quote and provided their phone number') and lists required fields. It advises what to ask the user before submitting (name, address, etc.). However, it lacks explicit 'when not to use' or comparison with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
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!