Skip to main content
Glama

Realestateapi

by io.github.pipeworx-io

AI Visibility Check

ai_visibility_check
Read-onlyIdempotent

Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYesThe thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing".
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com.
contextNoOptional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names.

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate a safe read-only, idempotent operation. The description adds important behavior: default free model, BYO key for Anthropic with direct billing, and return structure including per-model fields. No contradictions with 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 two sentences long, front-loaded with the core action, and every sentence adds meaningful information. No redundancy or unnecessary detail.

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 the tool has 4 parameters (all documented), no output schema but description explains return format, and annotations provide safety, the description is largely complete. It lacks error handling details but that is acceptable for a non-destructive tool.

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?

With 100% schema coverage, the baseline is 3. The description adds value beyond schema by explaining the default model for the 'models' parameter and providing examples for 'entity' and 'context'. It clarifies that '_apiKey' is only needed for Anthropic and how it's used.

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's purpose: probing LLMs to score visibility of an entity. It specifies the verb 'probe', resource 'LLMs', and output 'visibility (0-100)'. It distinguishes from sibling tools by focusing on AI model visibility rather than general entity info or research.

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 explicit use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to provide an API key for Anthropic. However, it lacks explicit mention of when not to use the tool or alternatives, though the context of sibling tools makes this less critical.

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

All Realestateapi 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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping research/lookup purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and validate_claim both verify claims against sources, and six polymarket_* tools overlap on edge/arbitrage detection. The three realestateapi_* tools are distinct but sit awkwardly beside 31 unrelated tools.

Naming Consistency2/5

Naming conventions are mixed: snake_case prefixed tools (realestateapi_property_search), domain-prefixed tools (polymarket_edges), verb-noun tools (ask_pipeworx, compare_entities), noun phrases (entity_profile, recent_changes), and bare verbs (remember, forget, recall). There is no consistent verb_noun pattern across the set.

Tool Count2/5

34 tools is heavy, and the vast majority belong to the Pipeworx platform rather than the Realestateapi identity — only 3 of 34 tools are real-estate specific. The count is not well-scoped for the server's stated purpose.

Completeness2/5

For a real estate API the surface is severely thin: search, detail, and skip-trace only, with no market trends, tax history, rental estimates, or comparable-sales data. For the Pipeworx meta-domain the coverage is broader, but the server presents as Realestateapi, making the domain coverage a mismatch.

Resources