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Analook — Competitor Intelligence

by io.github.Gingiris-1031

analyze_competitor

Submit a competitor analysis job.

Analyzes a competitor's website across 15+ data sources (SEO, traffic,
social, Product Hunt, GitHub, Wayback Machine history, AI-generated
insights, etc.) and returns a job_id. Use get_report_status(job_id) to
poll and get_report(job_id) to retrieve results when status='completed'.

Typical analysis takes 2-5 minutes. Requires authentication (deducts 1
credit from your Analook balance).

Args:
    url: Competitor website URL (e.g. 'https://linear.app' or 'lovable.dev')
    product_name: Optional product name override (defaults to domain)
    lang: Report language, 'en' (default) or 'zh' for Chinese output

Returns:
    {job_id: str, status: 'started', poll_url: str} on success
    {error: str, hint?: str} on auth/validation failure

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
langNo
contextYesExplain 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."
product_nameNo
conversation_idNoEcho 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.

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description fully discloses key behavioral aspects: it is an asynchronous job submission, requires authentication, deducts 1 credit, takes 2-5 minutes, and returns a job_id immediately. It also documents both success and error return formats.

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

Conciseness5/5

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

The description is well-structured with clear sections: intro, workflow, timing/auth, args, returns. Every sentence provides necessary information without fluff, and the most important action is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an async job submission tool with no output schema, the description is complete: it explains the input, the immediate response, the polling mechanism, the retrieval step, and error cases. It also mentions the breadth of data sources, setting expectations for report content.

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 value for url (with examples), product_name (defaults to domain), and lang ('en'/'zh'). However, the required 'context' parameter and optional 'conversation_id' are not mentioned, leaving a gap. Since schema description coverage is low (40%), the description only partially compensates.

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 submits a competitor analysis job and specifies what it does (analyzes website across 15+ data sources, returns a job_id). This distinguishes it from sibling reporting tools like get_report_status and get_report.

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

Usage Guidelines4/5

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

It explicitly explains the post-submission workflow: use get_report_status(job_id) to poll and get_report(job_id) to retrieve results. It also notes typical duration (2-5 minutes) and authentication/credit requirements. However, it does not explicitly contrast with run_growth_audit or other submission siblings.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct resource+action: analyze/run create jobs, get_report/get_report_markdown/get_growth_audit retrieve different output formats, status polling is separate, and the two listing tools (public vs personal) are clearly differentiated by name and description.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (analyze_competitor, browse_public_reports, get_report, run_growth_audit). The get_* verbs are consistently used for retrieval, and list_* for list operations.

Tool Count5/5

8 tools is well-scoped for a competitor intelligence API: two job submission types (competitor analysis, growth audit), retrieval in both JSON and Markdown, status polling, and listing/browsing. Each tool serves a distinct purpose with no redundancy.

Completeness4/5

The core lifecycle (submit → poll → fetch) is fully covered for both analysis types, with additional reporting and discovery features. Minor gaps exist (e.g., no cancel operation, no direct full-text retrieval of public reports), but they don't block primary workflows.

Resources