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Order deep report

order_deep_report

Order the paid deep report (5 EUR, one-off) for a business in a city index: ChatGPT, Claude and Gemini are asked ten questions about it the way people ask, every answer is kept in full with where it sent people, plus the nearest similar places, the fix plan with the kit to paste, what an agent reads on the site, a page and a PDF. Returns the order token and a Stripe checkout link for the person to pay (nothing is charged until they do); the report is written in about fifteen minutes after payment and also emailed, and its status and text can be read by the token.

Input Schema

Name	Required	Description	Default
`lang`	No	Language of the delivery mail: en, pt, es, fr, de, nl, it, da or cs	en
`email`	Yes	Where the report and the receipt go
`domain`	Yes	The business's registered domain, for example belcanto.pt; it must be in a city index (see city_leaderboard)
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 16, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "domain",
-  "email"
-]New value: +[
+  "domain",
+  "email",
+  "context",
+  "llm_model"
+]

AddedOct 14, 2026

TDQS

A3.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only cover the safety profile (readOnly=false, idempotent=false, openWorld=true). The description goes well beyond them, disclosing the price, that nothing is charged until the user pays, that a Stripe checkout link is returned, that the report is written in about fifteen minutes after payment, that it is emailed, and that its status/text is retrievable by token.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded in the opening clause and every clause carries decision-relevant content (price, deliverable, return values, turnaround). It is still a single very long, comma-heavy sentence, which costs some readability, so it falls short of a 5.

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?

With no output schema, the description carries the return-value burden well by stating it returns an order token and a Stripe checkout link, plus the post-payment lifecycle. It is complete for a checkout-creation tool; only minor wiring detail (e.g., the conversation_id sequencing) lives solely in the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 every parameter (lang, email, domain, context, llm_model, conversation_id). The description adds no parameter-specific syntax or format detail beyond tying the email param to delivery, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (order) plus the exact resource and price (the paid deep report, 5 EUR, one-off) scoped to a business in a city index. It implicitly distinguishes itself from the read-side siblings by noting the report 'can be read by the token', but it never names get_deep_report or get_site_report explicitly, so sibling differentiation is inferential rather than stated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The audience and prerequisite are implied (a business in a city index, matching the domain param's requirement), and the cost/checkout flow makes the 'when' reasonably clear. However, it never routes the agent to alternatives such as get_deep_report for reading an existing report or get_site_report, and gives no explicit when-not condition.

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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