Analook — Competitor Intelligence

run_growth_audit

Run a full Growth Audit — three linked strategic reports for a product.

Unlike analyze_competitor (a single 15-signal intelligence snapshot), a
Growth Audit produces an Executive Summary + a Diagnosis Report + a 30-day
Action Plan, grounded in real channel/tactic playbooks. Best for 'how do I
grow THIS product' rather than 'what is this competitor doing'.

Takes ~4-6 minutes. Requires authentication and deducts 10 credits. Poll
with get_growth_audit(job_id) until status='completed'.

Args:
    url: Product website URL to audit
    product_name: Optional product name override (defaults to domain)
    lang: Report language, 'en' (default) or 'zh'

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes
`lang`	No
`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.
`product_name`	No
`conversation_id`	No	Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedSep 18, 2026

changedInput schema / properties / context / description

Previous value: -"Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\""New value: +"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.\""

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: -[
-  "url",
-  "context"
-]New value: +[
+  "url",
+  "context",
+  "llm_model"
+]

Changed3 schema fields changedAug 14, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "url"
-]New value: +[
+  "url",
+  "context"
+]

First observedJul 9, 2026

TDQS

A4.5/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses the runtime (~4-6 minutes), authentication requirement, credit deduction (10 credits), and the asynchronous polling pattern. This goes well beyond the minimum required behavioral disclosure.

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 description is well-structured and front-loaded: purpose, differentiation, usage, behavioral notes, and args. Each sentence earns its place. The only deduction is the incomplete Args list, which omits three schema parameters and could mislead an agent relying solely on the description.

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?

The description explains the output deliverables (Executive Summary, Diagnosis Report, 30-day Action Plan), the async polling workflow, and the cost. However, it does not mention the required analytics parameters (context, llm_model) that the agent must supply, and with no output schema this is a notable gap in guiding a correct invocation.

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?

The description adds meaningful semantics for url, product_name, and lang, none of which have descriptions in the schema. However, it completely omits the required params context, llm_model, and conversation_id, which account for half the schema. With 50% schema coverage, the description only partially compensates for the missing parameter context.

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

Purpose5/5

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

The description clearly states the tool runs a full Growth Audit with three linked strategic reports, explicitly differentiating from analyze_competitor by naming the output components and the use case ('how do I grow THIS product'). This makes the purpose unambiguous and distinguishes it from siblings.

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

Usage Guidelines5/5

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

The description explicitly tells when to use this tool versus analyze_competitor ('Best for... rather than...') and explains the async workflow with get_growth_audit for polling. It also states the expected duration and cost, leaving no ambiguity about the usage context.

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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run_growth_audit

Input Schema

Schema Changelog

TDQS

Glama MCP Gateway

Resources