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Estat Japan

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare idempotent, readOnly, openWorld, and not destructive. The description adds that the tool probes each entity with ai_visibility_check, ranks by score, and returns a ranked list with score, confidence, and signal density per entity—providing meaningful behavioral context beyond the annotations.

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 three sentences long, front-loaded with the core action, followed by process details and a usage example. Every sentence adds value, with no redundancy or fluff.

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?

Given no output schema, the description adequately describes the return value (ranked list with score, confidence, signal density). It covers purpose, process, output, and use case. It does not mention error handling or prerequisites for using non-default models, but the schema addresses the API key requirement.

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 100% with descriptions for all parameters. The description adds value by clarifying that the first entity in the array is treated as the 'subject' and the rest as competitors, which is not in the schema. This aids correct invocation.

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 the tool compares AI visibility across multiple entities side-by-side, probes each with ai_visibility_check, ranks by score, and surfaces which is most/least recognized. This clearly distinguishes it from single-entity probes like ai_visibility_check and from general comparison tools like compare_entities.

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 provides a concrete use case ('competitive AI-marketing audits') and an example question. It implies this is for multi-entity comparison versus single-entity probing, but does not explicitly state when not to use or list alternatives.

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

A3.6/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the ask_pipeworx variants and Polymarket tools, but detailed descriptions help differentiate. Some tools like 'discover_tools' and 'suggest_questions' also have similar discovery roles, causing potential confusion.

Naming Consistency3/5

Names follow snake_case but lack a consistent pattern: some start with verbs (e.g., 'ask_pipeworx', 'compare_entities'), others with nouns (e.g., 'entity_profile', 'recent_changes'), and prefixes like 'pipeworx_' and 'polymarket_' are used sporadically, making the naming scheme mixed but still readable.

Tool Count2/5

35 tools is excessive for a server named 'Estat Japan', which should focus on Japanese statistics. The majority of tools are general-purpose Pipeworx tools, diluting the scope and making the count feel bloated for the stated purpose.

Completeness2/5

The e-Stat tools (list_data_catalog, search_stats, get_metadata, get_data) provide basic read-only access but lack update or delete operations. The inclusion of many unrelated tools leaves significant gaps for Japanese statistics, and the overall surface is incomplete for the server's implied domain.

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