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Postscript

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds: it passes the API key straight through to Anthropic (no storage), returns per-model {score, confidence, signals, raw_response} plus a 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 a compact paragraph with key information front-loaded. Each sentence adds value: purpose, default behavior, API key cost implication, return structure, and use cases. Could be slightly more structured (e.g., bullet list of return fields) but overall efficient.

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, the description compensates by listing return fields: 'per-model {score, confidence, signals, raw_response} + a combined view.' It also explains the optional API key and default model. Missing details like rate limits or a list of all supported models, but sufficient for most use cases.

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 description coverage is 100%, so baseline is 3. The description adds context beyond the schema: e.g., '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).' This clarifies the optional _apiKey parameter's cost implications.

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: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies the verb 'probe', the resource 'LLMs', and the output 'score visibility'. This distinguishes it from siblings like 'scan_competitor_ai_presence'.

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 context on when to use: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and optional API key for Anthropic, implying when to include that parameter. However, it does not explicitly mention when not to use or compare with specific sibling tools.

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

A4/5.0
Disambiguation4/5

Most tools have clear, distinct purposes with detailed descriptions that differentiate them. However, there is some overlap among data query tools (e.g., ask_pipeworx, deep_research, entity_profile) and among Polymarket analysis tools, which could cause confusion for an agent.

Naming Consistency2/5

Tool names lack a consistent pattern, mixing snake_case (ai_visibility_check, bet_research) with descriptive phrases (ask_pipeworx, generate_llms_txt) and some with verbs (list_subscriptions, remember). This inconsistency makes it harder to predict tool names.

Tool Count3/5

With 34 tools, the server covers a broad scope including data querying, Polymarket analysis, SMS management, and utilities. While many tools are justified, the number feels slightly high and some tools (e.g., multiple Polymarket tools) might be consolidated.

Completeness4/5

The server provides comprehensive coverage for data analysis, entity resolution, fact-checking, and monitoring. However, SMS management lacks create/update operations for keywords and subscribers, and there is no tool for sending SMS messages, indicating minor gaps.

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