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Glama

Realestate

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. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly and idempotent. Description adds that it calls ai_visibility_check internally, ranks results, and returns list with score, confidence, signal density. No contradictions.

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?

Three short sentences, front-loaded with main action. Every sentence earns its place: purpose, mechanism with use case, output description. No unnecessary words.

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, description adequately explains output format. Covers key aspects: ranked list, score, confidence, signal density. Does not cover error cases but overall sufficient.

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 has 100% description coverage. Description adds meaningful context: first entity treated as subject, competitors for comparison. Clarifies models parameter with instruction to omit for default.

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?

Description uses specific verb 'Compare AI visibility' and resource 'multiple entities side-by-side'. Clearly distinguishes from sibling tool ai_visibility_check by stating it probes each entity with that tool and ranks them.

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?

Provides concrete use case for competitive AI-marketing audits. Implies single entity checks should use ai_visibility_check. Could explicitly state when not to use, but the context is clear.

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

A3.8/5.0
Disambiguation2/5

Multiple tools occupy the same natural-language lookup niche: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions, and the beta tool is currently described as identical to the stable router. Prediction-market edge detection also fans out across bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, and polymarket_fill_risk, so an agent can easily select the wrong one.

Naming Consistency4/5

Most names follow a predictable snake_case action-first pattern (ask_pipeworx, resolve_entity, subscribe, unsubscribe) with helpful domain prefixes for polymarket_*, realestate_*, and pipeworx_*. Minor deviations exist—entity_profile is noun-first, ask_pipeworx lacks an underscore, and remember/forget/recall are bare verbs—but they do not create real confusion.

Tool Count2/5

33 tools is well above the coherence sweet spot and the rubric's 25+ threshold. The count is inflated by auxiliary platform utilities (feedback, trending, memory, subscriptions, llms.txt generation, npm scanning) that are unrelated to the Realestate name and make the tool surface feel like a full platform rather than a focused MCP server.

Completeness2/5

For a server named Realestate, the surface is only minimally complete: realestate_municipalities and realestate_transactions cover Japanese transaction lookups, but there are no tools for property listings, property details, pricing estimates, or typical real-estate workflows. Even viewed as a broad data platform, the set is read-heavy with no create/update/delete operations beyond memories and subscriptions, leaving significant workflow gaps.

Resources