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Transport Feeds

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.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, open-world behavior. The description adds cost implications (Anthropic key), default model, and output 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.

Conciseness5/5

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

The description is concise (3-4 sentences) and front-loaded with the main purpose. Each sentence adds relevant information: action, default, optional feature, use cases. No redundancy.

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?

The description adequately covers purpose, parameters, outputs, and use cases. Without an output schema, it effectively summarizes return fields. A slight gap is the lack of detail on the 'combined view', but overall sufficient.

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%, so baseline is 3. The description adds value by explaining default model, cost for Anthropic, and output structure hints beyond schema descriptions. Example usage context is provided.

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 it probes LLMs for brand visibility and scores it (0-100). It distinguishes from sibling tools like 'ask_pipeworx' or 'deep_research' by focusing on AI visibility audits for marketing. The use cases (pre-launch brand checks, competitive monitoring) add specificity.

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 (AI-marketing audits, pre-launch, competitive monitoring) and mentions default vs. paid model options. However, it lacks explicit when-not-to-use guidance or direct comparison to 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

A3.7/5.0
Disambiguation2/5

There is substantial overlap among the many question-answering tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, compare_entities, entity_profile, recent_changes) and the prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread). While each has nuanced differences, agents will struggle to select the right one, especially with several 'ask_pipeworx' variants that behave nearly identically.

Naming Consistency3/5

Most tool names use snake_case, but patterns vary widely: some are verb_noun (list_feeds, read_feed, subscribe, unsubscribe, remember), others are noun_phrases (polymarket_arbitrage, pipeworx_trending, entity_profile), and a few like 'ask_pipeworx' and 'bet_research' don't follow a consistent structure. The mixed conventions make prediction of new tool names difficult.

Tool Count2/5

With 34 tools, this is far too many for a server named 'Transport Feeds'. The majority of tools are unrelated to transport feeds, covering generic data research, prediction markets, and memory utilities. The count overwhelms any focused purpose and would require extensive discovery to navigate.

Completeness3/5

For the actual data-research and prediction-market functions, the surface is quite complete—covering lookups, comparisons, grounded verification, arbitrage scans, fill risk, trending, and subscriptions. However, for the declared domain (transport feeds), there are only two feed-specific tools (list_feeds, read_feed) with no write/update/delete operations, leaving obvious gaps.

Resources