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Federal Register

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds context beyond annotations: default model (Workers AI Llama-3.3-70b, free), the need for an API key for Anthropic (with cost implication), and the return structure (per-model score, confidence, signals, raw_response plus combined view). No contradictions.

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 front-loaded with the primary action and output, followed by default behavior, API key condition, return format, and use cases. It is efficient with no redundancy, though slightly lengthy at 4 sentences.

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

Completeness3/5

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

Given no output schema, the description partially covers return values (per-model object with score, confidence, signals, raw_response, plus combined view) but does not detail what 'signals' or 'raw_response' contain, nor does it mention error handling or rate limits. More detail on output structure would improve completeness.

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 parameter descriptions. The description adds meaning beyond schema by specifying default model, explicitly listing supported models ('workers-ai' and 'anthropic'), explaining that _apiKey is passed directly to Anthropic API, and clarifying that context helps disambiguate entities.

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 a specific verb 'Probe' and clearly identifies the resource (LLMs for knowledge about entities). It explicitly states the output (score 0-100 per model) and distinguishes itself from siblings by specifying the functionality of scoring visibility per model, which is not present in similar tools 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 clear use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to pass an API key for Anthropic probing. However, it does not explicitly mention when not to use this tool or contrast it with sibling tools like 'scan_competitor_ai_presence'.

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

A3.5/5.0
Disambiguation1/5

Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, and ask_pipeworx_grounded overlaps heavily with them. Additionally, entity_profile, compare_entities, deep_research, and validate_claim all cover similar company/factual research territory, creating frequent selection ambiguity.

Naming Consistency2/5

Naming mixes multiple conventions: descriptive lowercase phrases (ai_visibility_check, compare_entities, valid claim) coexist with verb_noun (search_documents, recent_rules) and inconsistent underscores (ask_pipeworx vs ask_pipeworx_grounded, resolve_entity). There is no single recognizable pattern.

Tool Count1/5

The server is named 'Federal Register' but only 3 of 34 tools (search_documents, recent_rules, get_document) relate to that domain. The other 31 tools form a sprawling Pipeworx data and prediction-market suite, making the count extreme and inappropriate for the declared purpose.

Completeness2/5

For the stated Federal Register domain, the surface is minimal: search, recent listing, and single-document retrieval, with no docket browsing, full-text search within documents, or agency-specific navigation. The broader Pipeworx capability set is comprehensive but irrelevant to the server's name, leaving obvious gaps for the actual purpose.

Resources