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Worms

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=true, idempotentHint=true, openWorldHint=true, destructiveHint=false. The description adds value by explaining the default free model, the BYO key mechanism for Anthropic, and the per-model return structure, exceeding what annotations provide.

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 three sentences, front-loaded with the core action, and every sentence adds unique value. No fluff or redundant information.

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?

Given no output schema, the description adequately explains return values (per-model {score, confidence, signals, raw_response} + combined view). All four parameters are described sufficiently for an agent to invoke the tool correctly.

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 description coverage is 100%, but the description adds meaning beyond the schema: it explains default values ('workers-ai' for models), the purpose of _apiKey (BYO key for Anthropic), and how context helps disambiguate, enriching semantic understanding.

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 specifies a clear verb ('Probe') and resource ('LLMs for what they know about a business / brand / product / topic'), and distinguishes from siblings by focusing on AI visibility scoring. It provides specific output details and use cases.

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 explicitly states when to use the tool (AI-marketing audits, pre-launch brand checks, competitive monitoring) and provides context on default vs paid model probing. However, it does not mention when not to use it or directly contrast 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.

All Worms toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have functionally overlapping purposes, most notably ask_pipeworx and ask_pipeworx_beta, which currently behave identically. The many prediction-market tools are each distinct but still leave boundaries that require careful reading, and the query/research tools (ask_pipeworx, deep_research, potentially validate_claim) have an ease of being confused. Fine verbal descriptions reduce but do not eliminate the ambiguity for an agent.

Naming Consistency3/5

All names are lowercase snake_case and many follow a verb_noun pattern, such as resolve_entity, get_classification, and list_subscriptions. However, the set is inconsistent overall: it mixes single verbs (remember, forget, subscribe), noun-style names (entity_profile, recent_alerts), and several prefixed families (pipeworx_*, polymarket_*). It's readable but not a coherent, uniformly applied convention throughout.

Tool Count2/5

34 tools in one server is well beyond the generally well-scoped 3–15 range. The server appears to bundle several unrelated domains together—WoRMS taxonomy, Pipeworx data research, Polymarket analysis, memory utilities, and subscriptions—creating avoidable cognitive load and making selection more difficult. Splitting into targeted servers would greatly improve the interface.

Completeness2/5

The server is named 'Worms' and includes a WoRMS taxonomy subdomain, but that subdomain only supports searching, classification, and common names—the rest of the marine taxonomy surface is missing (e.g., direct AphiaID record lookup, distributions, synonyms, hierarchical children). The remainder of the tools serve an entirely different data-research purpose, so the server is incomplete relative to its name AND the bundled extra domains add confusion rather than a coherent cohesive coverage.

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