Skip to main content
Glama

Analook — Competitor Intelligence

by io.github.Gingiris-1031

get_growth_audit

Fetch a Growth Audit's three reports (Executive Summary, Diagnosis, Action Plan) as Markdown.

Args:
    job_id: ID from run_growth_audit() (starts with 'ga-')

Returns:
    {status, reports: {executive_summary, diagnosis_report, action_plan}}
    while running, only {status, progress} is returned.

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

A4/5.0
Behavior3/5

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 does disclose important runtime behavior: 'while running, only {status, progress} is returned.' It also specifies the return shape. But it omits error handling, required permissions, or what happens if the job fails or is not found, leaving significant behavioral gaps.

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 compact and structured with Args/Returns sections. Every sentence contributes either the tool's main behavior, the parameter key detail, or the async return behavior. No redundant or vague text.

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 supplies a clear return structure and explains the progress state, which is essential for an asynchronous job fetch. It also links the job_id source. Missing details include error statuses and how to interpret 'completed' vs 'failed', but for a fetch operation this is largely sufficient to use correctly.

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 description coverage is 67%, and the description compensates for the undocumented `job_id` parameter by explaining its origin and format ('ID from run_growth_audit() (starts with 'ga-')'). This adds meaning beyond the schema, which lists `job_id` with no description. The other parameters have detailed descriptions in the schema, so the incremental value from the description is moderate.

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 action: 'Fetch a Growth Audit's three reports... as Markdown.' It names the exact resource (Growth Audit) and the output format (Markdown), and the `job_id` reference to `run_growth_audit()` distinguishes it from sibling tools like `get_report` or `get_report_markdown`.

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 description implies usage context by specifying that `job_id` comes from `run_growth_audit()`, and it mentions the progressive return behavior while running. However, it does not explicitly contrast this tool with alternatives like `get_report_status` or state when to use this versus `get_report_markdown`. Usage guidance is present but implicit.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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