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Cfpb

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, etc. Description adds value by specifying cost implications (BYO key for Anthropic), default model, and return structure (per-model {score, confidence, signals, raw_response} + 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.

Conciseness5/5

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

Three sentences: first states main action and return, second covers model options and cost, third lists use cases. No redundant information. Every sentence adds value.

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?

No output schema, but description adequately covers return structure. It explains default model and optional Anthropic probing. Could mention parallel probing or response time, but overall complete for a probe tool with 4 parameters and good annotations.

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%, baseline 3. Description adds significant meaning: explains 'entity' as brand/business topic, lists model options, clarifies '_apiKey' for Anthropic pass-through, and 'context' for disambiguation. This goes beyond schema descriptions.

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 LLMs for brand visibility and scores (0-100) per model. Verb 'probe' is specific, resource is LLMs, and it distinguishes from sibling tools like 'scan_competitor_ai_presence' by focusing on entity-specific knowledge scoring.

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 explicitly lists use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and when to provide an API key for Anthropic. However, it does not explicitly state when NOT to use it or compare with alternative tools among siblings.

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

A3.6/5.0
Disambiguation3/5

There are multiple overlapping tools for querying data (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, bet_research) that could confuse an agent, though descriptions are detailed enough to distinguish most. Tools like polymarket_edges and polymarket_edge_tracker are closely related, and ai_visibility_check vs scan_competitor_ai_presence overlap.

Naming Consistency2/5

Naming is highly inconsistent: some follow verb_noun (cfpb_search_complaints, resolve_entity, subscribe, recall), but many use varied patterns like adjectives (ai_visibility_check), imperative phrases (ask_pipeworx, scan_competitor_ai_presence), or compound/specialized names (polymarket_arbitrage, generate_llms_txt). No consistent prefix or convention is used across the toolset.

Tool Count2/5

With 36 tools spanning diverse domains (prediction markets, SEC filings, CFPB complaints, AI visibility, npm packages, IPC subscriptions), the server is sprawling and over-scoped. Many tools are specialized niche additions (polymarket_fill_risk, scan_dependency, generate_llms_txt) that expand the count without strong cohesion.

Completeness3/5

The toolset covers core areas well (entity resolution, profile, comparison, recent changes, search, claims verification, subscriptions). However, gaps exist: no update/delete for CFPB complaints (read-only), no direct raw SEC filing retrieval, and some lifecycle operations (e.g., editing subscriptions) are missing.

Resources