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Glama

Property Records

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

A4.5/5.0
Behavior5/5

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

Adds operational details beyond annotations: default model (Workers AI Llama-3.3-70b), cost implications (BYO key, direct payment to Anthropic), and return format. Annotations already cover read-only/idempotent safety, and the description enriches with auth and payment context.

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?

Four sentences, each earning its place: purpose, default/cost, return format, use cases. Front-loaded with core function and no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, description clearly specifies per-model result fields and combined view. Combined with rich annotations and 100% schema coverage, it is complete for a read-only tool.

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

Parameters3/5

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

Schema coverage is 100% and already describes default model, key requirements, and supported values. Main description adds minimal new info (exact model name), so baseline 3 is appropriate.

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 clearly states it probes one or more LLMs and scores visibility (0-100) per model, with specific verb ('probe'), resource (LLMs), and output. It distinguishes itself from sibling tools like scan_competitor_ai_presence by focusing on score-based visibility across multiple models.

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 clear use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and cost guidance (free default vs BYO Anthropic key). Does not explicitly mention when not to use or name alternatives, but context is strong.

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

All Property Records toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical variants of the same router, and there are six polymarket-related tools with overlapping arb/edge/fill-risk purposes. Property-specific tools are distinct but buried among many unrelated meta-tools.

Naming Consistency3/5

All names use snake_case, but the structural pattern is inconsistent: some are verb_noun (ask_pipeworx, validate_claim), others noun_verb (property_lookup), and many are noun_noun (entity_profile, polymarket_arbitrage). No clear systematic convention across the set.

Tool Count2/5

33 tools is far too many for a server labeled 'Property Records'—only two tools (property_lookup, property_coverage) actually serve that purpose. The rest belong to unrelated domains (general data lookup, prediction markets, memory, subscriptions), making the surface feel bloated and unfocused.

Completeness2/5

For a property-records server, the surface is incomplete: it only provides lookup plus a coverage matrix, with no other property-related operations (e.g., tax history, comparable sales) and no way to handle unsupported jurisdictions beyond a simple flag. The unrelated tools do not contribute to the stated domain, leaving the core purpose thinly covered.

Resources