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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

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.

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TDQS

A3.5/5.0
Disambiguation2/5

Multiple tool clusters blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route natural-language questions to sources with overlapping response shapes. The Polymarket family is better differentiated, but an agent faces real selection risk among the query family.

Naming Consistency3/5

All tools use snake_case, which is good, but verb conventions vary widely: ask_/get_/search_/list_/validate_/generate_/scan_ plus noun-only names like ai_visibility_check, entity_profile, bet_research, and the pipeworx_* prefix. The pattern is readable but not predictable enough to guess a tool's function from its name alone.

Tool Count2/5

34 tools is well past the 25+ threshold for a coherent tool set. The server tries to be a universal data gateway, prediction-market toolkit, memory store, subscription manager, WoRMS lookup, and dependency scanner all at once—scope creep that makes the surface unreasonably large.

Completeness2/5

For a data-access server there are notable holes: pipeworx:// citation URIs are advertised as fetchable but no tool resolves them directly; subscriptions can be created, listed, and cancelled but not updated or paused; and the WoRMS component (which matches the 'Worms' server name) has only three lookup tools with no synonym/distribution/export coverage.

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