Skip to main content
Glama

Json

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

A4.7/5.0
Behavior5/5

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

Annotations indicate read-only, open-world, idempotent, non-destructive. Description adds specifics: default model is free Workers AI Llama-3.3-70b, Anthropic probing requires BYO API key with direct billing, and return format per-model.

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?

Two sentences: first covers core functionality and default, second covers optional key and return structure. Front-loaded, no wasted words.

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?

For a 4-param read-only tool with no output schema, description explains return structure (per-model objects with score, confidence, signals, raw_response + combined view) and cost behavior. Complete for agent use.

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% (all 4 params described). Description adds value beyond schema by clarifying cost implications for _apiKey and disambiguation purpose for context.

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 the tool probes LLMs for brand visibility and scores it (0-100). It distinguishes from siblings like ask_pipeworx which focus on specific queries about Pipeworx.

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?

Description provides use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default vs. paid model usage but doesn't explicitly contrast with sibling tools or state when not to use.

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

All Json 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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from querying data (ask_pipeworx) to entity profiling (entity_profile) to prediction market analysis (polymarket_arbitrage, polymarket_edges). Even similar tools like deep_research and ask_pipeworx are differentiated by scope (single vs. multi-facet). There is no ambiguity.

Naming Consistency4/5

The vast majority of tools use snake_case (e.g., validate_claim, compare_entities). However, a few tools are single words (forget, recall, remember, subscribe, unsubscribe) which breaks the pattern slightly. This is a minor inconsistency.

Tool Count4/5

With 33 tools, the set is on the larger side but well-justified by the broad scope of the server (data querying, entity research, prediction markets, JSON utilities, subscriptions, etc.). Each tool serves a specific need, making the count appropriate.

Completeness5/5

The tool surface covers virtually all expected operations for the domain: querying, profiling, comparison, change tracking, validation, discovery, subscriptions, memory, and utilities. No obvious gaps are present for the intended use case of accessing Pipeworx data and related tasks.

Resources