Ask a US local business a current question by phone. Typed, timestamped answers for agents; x402.
Final structured answer with typed facts, evidence excerpts, timestamps, confidence and limitations. If the job is not final the response is provisional (http_status 202).
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| job_token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It explicitly reveals that a non-final job returns a provisional response with http_status 202, which is important and non-obvious. It also describes the response contents. It does not discuss auth or rate limits, but for a retrieval tool the key behavior is covered.
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, and the conditional provisional behavior is stated compactly in the second sentence. 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?
The description covers the main return contents and the provisional 202 behavior, which is helpful given there is no output schema. However, it leaves parameter semantics unexplained and gives no guidance on polling or checking finality via the status sibling, so the overall context is not fully complete.
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% and the description does not mention job_id or job_token at all. The names are mildly self-explanatory, but there is no explanation of their relationship, format, or how to obtain them, so the description adds no parameter meaning beyond the schema.
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: it returns the final structured verification answer, enumerating typed facts, evidence excerpts, timestamps, confidence, and limitations. This clearly distinguishes it from the sibling get_verification_status, which presumably returns only status information, and from the purchase/quote siblings.
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 implies this is the tool to call to retrieve the full verification result once a job exists, and it warns that a non-final job yields a provisional 202 response. However, it does not explicitly say when to use this versus get_verification_status, nor does it mention checking status before calling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Progress/state of a job. Poll every 15-30 seconds; live calls take minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| job_token | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses latency and polling cadence, but omits whether the operation is read-only, what the response contains, or error handling. Minimal behavioral disclosure beyond the polling hint.
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 wasted words. Purpose is front-loaded, followed by a practical polling note. It is appropriately brief, though brevity comes at the cost of parameter and behavioral detail.
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 tool is simple, but with no output schema and no parameter descriptions, the agent lacks information about the response format or how to interpret status values. The polling advice is helpful, but the definition is incomplete for 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 0% and the description offers no explanation of job_id or job_token. An agent cannot infer their purpose or format beyond the parameter names, and the description does nothing to compensate.
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 'Progress/state of a job' with the verb 'get' and a specific resource, distinguishing it from result retrieval. However, it doesn't explicitly differentiate from sibling tools like get_verification_result, relying on the name to imply status vs. result.
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?
It provides actionable polling guidance ('Poll every 15-30 seconds; live calls take minutes'), which implies when to call it repeatedly. But it doesn't state when to use this tool versus get_verification_result or any other alternative, leaving the selection logic to the agent's inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pay for a quote and start the asynchronous verification job. Returns job_id and job_token (keep it; it is shown once). If this server has no payment capability, returns the x402 payment requirements (HTTP 402 body) so you can pay with your own payment tool and retry the POST /v1/jobs request with a PAYMENT-SIGNATURE header.
| Name | Required | Description | Default |
|---|---|---|---|
| quote_id | Yes | ||
| idempotency_key | No | Client-generated UUID; reuse it when retrying |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It discloses that the job is asynchronous, that job_token is shown only once, and that a 402 fallback returns payment requirements. It does not cover failure modes or side effects beyond payment, but it covers the most important behaviors.
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, front-loaded with the primary action, and every sentence adds necessary information. The conditional fallback is technical but essential and clearly explained.
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 return values despite lacking an output schema, covers the asynchronous nature, and details the payment fallback. It could mention how to use the returned job_id/job_token with sibling status/result tools, but that is inferable from the sibling names.
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 description links quote_id to the quote being paid for, adding meaning beyond the bare schema field. idempotency_key is already described in the schema, so the description's indirect mention of retry is acceptable. The 50% schema coverage is partially compensated by the description.
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 action: paying for a quote and starting an asynchronous verification job. It is distinct from sibling tools that quote or retrieve results, and it specifies the key outputs (job_id, job_token).
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 context is clear: this is used after obtaining a quote and before checking verification status/results. It also explains the fallback flow when payment is not supported, though it does not explicitly name sibling tools as 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.
Get a free price to ARRANGE something at a specific US local business: book an appointment, hold an item, request a formal quote, or cancel a prior booking. This creates a real obligation, so it needs explicit authorization, the customer's name and callback number, and a maximum price the caller may agree to. The caller never negotiates, never pays, and never substitutes something different. Purchase the returned quote with purchase_real_world_answer.
| Name | Required | Description | Default |
|---|---|---|---|
| what | Yes | Exactly what to arrange, e.g. 'two Michelin Pilot Sport 4S 235/40R19 installed' | |
| when | No | Time window, e.g. 'tomorrow afternoon' | |
| notes | No | Constraints the caller must honour | |
| action | Yes | ||
| place_id | No | Google place_id, if known - more reliable than a name | |
| quantity | No | ||
| reference | No | Existing confirmation reference. Required for cancel. | |
| authorized | Yes | You confirm the end user authorized this commitment on their behalf | |
| postal_code | Yes | ||
| business_name | Yes | The business to act on | |
| customer_name | Yes | Name to hold the booking under. Shared with the business. | |
| max_price_minor | No | Ceiling in cents the caller may agree to. Required for book and hold. | |
| customer_callback_phone | Yes | Number the business can call back. Shared with the business. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does it well: it discloses that the call creates a real obligation, requires explicit authorization, and that the caller never negotiates, never pays, and never substitutes a different item. This goes well beyond the schema and alerts the agent to the side effects.
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?
Three sentences, front-loaded with the core purpose, and each sentence adds value — operation, prerequisites/constraints, and the follow-up purchase tool. No filler or repetition of schema details.
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, the description explains the return in practical terms ('Get a free price' and 'Purchase the returned quote'). It covers the key safety context, though it doesn't describe the exact quote response shape or mention all required inputs like postal_code; the rich input schema covers most of that.
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 77%, so the schema already documents most parameters. The description adds plain-language meaning for the action enum and groups required fields, but it doesn't explain postal_code or quantity, and the max_price explanation largely restates the schema's 'ceiling in cents' description.
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 names a specific operation — get a free price to arrange something at a US local business — and enumerates the four actions (book, hold, request_quote, cancel). It points to purchase_real_world_answer as the follow-up, but it never mentions the near-identically named sibling quote_real_world_answer, so full sibling disambiguation is missing.
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?
It clearly frames this as the quoting step and instructs the caller to purchase the returned quote with purchase_real_world_answer. It also signals that explicit authorization and a max price are prerequisites, but it doesn't state exclusions or compare with get_verification_result/status or quote_real_world_answer.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a free, immutable price quote for verifying a current fact about a US local business (stock, price, availability, capability, policy, wait time). Returns quote_id, price in integer minor units, scope, expected_completion_seconds and expires_at. Use purchase_real_world_answer to buy it.
| Name | Required | Description | Default |
|---|---|---|---|
| limits | No | ||
| request | Yes | Natural-language question, e.g. 'Does ABC Tire in 33312 have two Michelin Pilot Sport 4S 235/40R19 in stock today and what is the installed price?' | |
| location | Yes | ||
| callback_url | No | ||
| verification_mode | No | auto reuses fresh cached facts when safe; live_verified forces a new call |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'free' and 'immutable', and lists return fields including expected_completion_seconds and expires_at, giving some sense of the quote's characteristics. However, it does not explain that this is only a quote and not the actual verification, nor does it address rate limits, auth requirements, or what happens if the quote expires. It also does not clarify the relationship between quote and purchase beyond 'use purchase_real_world_answer to buy it'. This is adequate but not thorough.
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, front-loading the core purpose and return fields, then providing a clear next-step instruction. Every sentence earns its place with no redundancy or fluff.
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 tool has 5 parameters, nested objects, and no output schema. The description lists return fields but does not explain how the quote_id is used, the role of callback_url, or the semantics of limits and location beyond what the schema implies. It also doesn't clarify the verification_mode values or edge cases like expired quotes. While it provides a solid overview, it leaves critical operational details unresolved for an agent to call correctly.
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 only 40%, and the description does not compensate. The schema provides descriptions for request and verification_mode, but parameters like limits, location, and callback_url lack descriptions. The tool description does not explain these parameters, their constraints, or how they affect the quote. This is a significant gap given the low coverage and nested object complexity.
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 function: obtaining a free, immutable price quote for verifying a fact about a US local business. It specifies the verb ('Get'), the resource ('price quote'), and the scope (current fact about US local business). It also distinguishes from sibling tools like quote_real_world_action by focusing on 'answer' vs 'action'.
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 directs the user to purchase_real_world_answer for buying the quote, providing a clear next step. It implies this tool is for quoting, not for the actual verification result. However, it doesn't explicitly state when not to use it or mention alternatives like get_verification_status or get_verification_result for checking status, which are siblings. The guidance is useful but could be more comprehensive.
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.
get_verification_resultget_verification_statuspurchase_real_world_answerquote_real_world_actionquote_real_world_answerClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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Made-to-order data for AI agents: company intel, B2B contacts, scraping. Pay per call via x402.
Agent-native CRM + get-booked platform. Operate over MCP, or pay per call via x402. No login.
AI agent that calls, texts & emails businesses for you, then returns transcripts and replies.
Deterministic company, entity and location lookups for agents - per call, x402 or API key.
Add one secure layer between your agents and this server.
Tools are mostly distinct: status vs result, quote vs purchase, and answer vs action are clearly separated. The only mild ambiguity is that purchase_real_world_answer is also the purchase path for action quotes, not just answer quotes.
All names follow snake_case verb_noun and the get/quote/purchase verbs are used predictably. The slight inconsistency is purchase_real_world_answer covering both answers and actions, while quote_real_world_action suggests a separate action purchase tool.
Five tools is well-scoped for this server: quote, purchase, status, and result form a tight asynchronous workflow, with the second quote tool adding physical actions without bloat.
The quote-purchase-status-result lifecycle is complete for verified answers, and action quotes include cancellation capability. A dedicated confirm/cancel endpoint for purchased actions would be slightly cleaner, but the current surface supports the core workflow.
