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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.

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds behavioral context: default model (free), costing model for Anthropic (users pay directly), and the return format. This complements the annotations well.

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 a highly efficient 4-sentence paragraph. It is front-loaded with the core action, then progressively adds key details (default model, API key use, return format, use cases). No superfluous text.

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 adequately describes the return format (per-model {score, confidence, signals, raw_response} + combined view). It covers the tool's purpose, parameters, and use cases, making it complete for an agent to decide when and how to invoke it.

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 beyond schema by explaining the supported models for the 'models' parameter ('workers-ai', 'anthropic') and clarifying that _apiKey is passed through to api.anthropic.com, with 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 uses precise verbs ('Probe', 'score visibility') and specifies the resource ('one or more LLMs'). It clearly distinguishes the tool from siblings by focusing on cross-model visibility scoring, with specific use cases like AI-marketing audits.

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 explicit use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains when to use the _apiKey parameter (only for Anthropic). However, it doesn't mention when NOT to use this tool or suggest alternative tools, which would have earned a 5.

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
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, and validate_claim all route factual questions through similar pipelines. The polymarket_* cluster also blurs together, with arbitrage, edges, fill_risk, kalshi_spread, and bet_research all analyzing prediction-market mispricings from different angles.

Naming Consistency3/5

All names use consistent snake_case, but the verb/noun pattern is mixed: some are verb-first (compare_entities, generate_llms_txt, validate_claim), others noun-first (polymarket_edges, entity_profile, ai_visibility_check), and some are bare product names (ask_pipeworx, pipeworx_trending). Readable overall, but no single predictable convention.

Tool Count2/5

34 tools is far too many for a coherent server, especially since the server is named 'Sgd' but only 3 tools relate to yeast genetics. The remaining 31 tools span data lookup, prediction markets, memory, subscriptions, npm scanning, and llms.txt generation—an unfocused grab bag that should be split into multiple servers.

Completeness2/5

As an SGD yeast-genome server, the surface is thin: search, get_gene, and get_gene_go cover basic lookup but miss sequences, interactions, strains, homologs, and other standard SGD data. As a general data utility, the collection is broad but incoherent, with several one-off tools (generate_llms_txt, scan_dependency) that have no connection to the rest.

Resources