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Analook — Competitor Intelligence

by io.github.Gingiris-1031

get_report

Fetch the full competitor analysis report as structured JSON.

Reports contain: website snapshot, Wayback Machine history, SEO/traffic
data (DataForSEO), social media presence, Product Hunt launches, GitHub
stats, pricing, funding, AI-generated business insights, growth
playbooks, and more.

Args:
    job_id: ID from analyze_competitor(); status must be 'completed'

Returns:
    The full report dict (nested structure), or {error} if not found / not ready.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
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."
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

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden. It discloses the return type (full report dict), error semantics ({error} if not found/not ready), and prerequisites (completed status). This is solid behavioral transparency for a read-only fetch operation, though it doesn't mention side effects or rate limits—acceptable given the nature of the tool.

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 with Args and Returns sections. The opening sentence is direct. The long list of report contents is informative and helps set expectations, though it could be trimmed without losing core value. Overall, it is appropriately sized for the tool's complexity.

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?

In the absence of an output schema, the description explains the return value as a nested dict and lists its major components. It also covers error behavior. This is sufficient for an agent to understand the tool's outputs and prerequisites. It doesn't describe exact nesting, but that is not necessary for successful invocation.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% because context and conversation_id have descriptions. The tool description adds crucial semantics for job_id: 'ID from analyze_competitor(); status must be completed', which the schema lacks. This goes beyond the structured schema and compensates for the missing description.

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 states a specific verb and resource: 'Fetch the full competitor analysis report as structured JSON'. It clearly identifies the tool's output format and scope, distinguishing it from siblings like get_report_status (status) and get_report_markdown (presumably Markdown format).

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?

The description gives clear usage context: job_id comes from analyze_competitor() and the status must be 'completed'. This implies a workflow and precondition. However, it does not explicitly mention alternative tools or when not to use this tool, so it stops short of full guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Analook — Competitor Intelligence toolsInspect
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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.

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