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Worms

by io.github.pipeworx-io

Scan Competitor AI Presence

scan_competitor_ai_presence
Read-onlyIdempotent

Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelsNoWhich models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai.
_apiKeyNoOptional Anthropic API key — only if "anthropic" is in models. Passed to api.anthropic.com per probe.
contextNoOptional shared context applied to every probe (e.g. "B2B SaaS", "Boston restaurant"). Disambiguates common names.
entitiesYesArray of 2-8 entities to compare (brand/business/product names). First entry treated as the "subject" for narrative; rest are competitors.

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive. The description adds that the tool probes each entity, ranks them, and returns a ranked list with score, confidence, and signal density. It also mentions the optional _apiKey for Anthropic, providing useful behavioral context.

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, consisting of three sentences that efficiently convey the tool's function, use case, and output. It is front-loaded with the core purpose and no wasted words.

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 the tool has no output schema, the description clearly describes the output format: 'ranked list with score, confidence, signal density per entity.' It also explains the workflow and parameter usage, making it complete for an agent to understand and invoke 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 coverage is 100%, so baseline is 3. The description adds context by stating that the first entity is treated as the 'subject' for narrative and that all probes use ai_visibility_check. This adds meaning beyond the schema's parameter descriptions.

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 the purpose: 'Compare AI visibility across multiple entities side-by-side.' It specifies it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. This distinguishes it from the sibling ai_visibility_check which likely handles single entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides usage context: 'useful for competitive AI-marketing audits' and gives an example question. However, it does not explicitly state when not to use this tool or mention alternatives like compare_entities. It lacks exclusion criteria.

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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