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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. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds value by disclosing the default free model (Workers AI), the need for a BYO API key for Anthropic probes, and the return structure (per-model fields + combined view). 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (three sentences) and front-loaded with the primary action. It efficiently covers purpose, defaults, key parameter, and use cases without redundant words. A slightly more structured layout could improve readability, but it remains effective.

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, the description clearly outlines the return format (per-model score, confidence, signals, raw_response + combined view), cost implications, and optional parameters. Given the tool's moderate complexity (4 params), the description is sufficiently complete for agent invocation.

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 all parameters described. The description adds meaning beyond the schema by explaining default model behavior, the purpose of _apiKey, and how the context parameter disambiguates entities. This helps the agent understand parameter usage beyond basic definitions.

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 uses specific verbs ('probe', 'score') and clearly identifies the resource (LLMs for entity knowledge) and output (visibility score 0-100). It differentiates from siblings by focusing on AI visibility scoring, a unique functionality not offered by other tools like ask_pipeworx or bet_research.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context ('useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') but does not explicitly state when to not use the tool or suggest alternatives among siblings. The agent can infer appropriate scenarios but lacks clear exclusion criteria.

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

ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical right now, making them near-duplicates. ai_visibility_check and scan_competitor_ai_presence also heavily overlap, and deep_research vs ask_pipeworx requires careful reading to know which to pick.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (list_templates, create_*, validate_claim). However, a few bare verbs break the pattern: compose, forget, recall, and remember.

Tool Count1/5

34 tools is already heavy, but the server is named 'Gitignore' and only 3 of 34 tools relate to .gitignore templates. The other 31 tools form a completely unrelated data platform, making the count an extreme mismatch for the apparent scope.

Completeness3/5

As a data platform, the surface is broad but has notable gaps: citations return pipeworx:// URIs yet no fetch/read-by-URI tool exists, and subscriptions support subscribe/unsubscribe/list but not update. For the gitignore name, only basic template list/get/compose is present, with the rest irrelevant to the stated purpose.

Resources