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Wikipathways

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

A4.3/5.0
Behavior4/5

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

Annotations already convey read-only and non-destructive behavior. The description adds value by disclosing the cost model (free default vs. BYO Anthropic key), default model selection, and return structure (per-model and combined view). It does not cover rate limits or failure modes, but the annotation hints cover the safety profile 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 five sentences with zero waste. Purpose is front-loaded, followed by default behavior, optional model usage, return format, and use cases. Every sentence earns its place.

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 lacking an output schema, the description explicitly lists the return fields (score, confidence, signals, raw_response per model, plus combined view). All 4 parameters are explained in the description or schema. The typical workflow (choosing models, providing API key) is covered, making the tool self-contained for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear parameter descriptions. The description adds contextual nuance (e.g., who pays for Anthropic calls) but does not substantially enhance understanding beyond the schema. This matches the baseline for high-coverage schemas.

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 specific verbs ('probe', 'score') and clearly identifies the resource (LLMs for business/brand/product/topic visibility). It distinguishes from sibling tools by focusing on AI visibility scoring across multiple models, with explicit use cases for AI-marketing audits and competitive monitoring.

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 usage contexts (AI-marketing audits, pre-launch brand checks, competitive monitoring) and dictates when to use the default free model vs. Anthropic with BYO key. However, it lacks explicit 'when not to use' comparisons with sibling tools like deep_research or compare_entities, which would strengthen guidance.

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

B3.3/5.0
Disambiguation1/5

The tool set is severely mismatched: only 3 of 34 tools (get_pathway, list_pathways, search_pathways) relate to WikiPathways while the rest form overlapping Pipeworx/Polymarket families (ask_pipeworx vs ask_pipeworx_beta vs ask_pipeworx_grounded; multiple polymarket_* tools) with unclear boundaries and overlapping purposes.

Naming Consistency2/5

Naming conventions are highly inconsistent: product-specific names (pipeworx_feedback, pipeworx_trending), generic memory verbs (remember, recall, forget), and mixed snake_case patterns with no unifying verb_noun structure. The names do not reflect the WikiPathways domain at all.

Tool Count1/5

34 tools is far too many for a WikiPathways server, which only needs a handful of pathway-related operations. Nearly all tools belong to unrelated domains (SEC, FDA, Polymarket, npm, etc.), making the set feel bloated and unfocused.

Completeness1/5

For the stated WikiPathways purpose, only get, list, and search are present—no create, update, or delete operations—leaving obvious lifecycle gaps. The extensive non-WikiPathways tools do not contribute to the server's apparent domain coverage and create dead ends for agents expecting pathway management.

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