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Crystallography

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.

TDQS

A4.5/5.0
Behavior4/5

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

The description discloses that it probes each entity using ai_visibility_check and ranks by score, explaining the mechanism beyond annotations which already indicate read-only, idempotent, and non-destructive behavior. It also explains the Anthropic API key dependency. No contradictions with annotations.

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?

Three sentences with no fluff: clear purpose, example usage, and output format. Front-loaded with the main action and value proposition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description explains return format (ranked list with score, confidence, signal density). However, it does not reinforce the schema constraint of 2-8 entities or mention error handling for invalid inputs, leaving minor gaps.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds critical context: first entity is treated as subject for narrative, context disambiguates common names, and models have defaults and dependencies explained. This adds significant value beyond the schema.

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 tool compares AI visibility across multiple entities side-by-side, using a specific verb (compare/probe) and resource (AI presence). It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general comparison) by focusing on AI visibility ranking.

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?

Provides a concrete use case (competitive AI-marketing audits) and an example question. However, it does not explicitly say when not to use or mention alternatives for single entity checks, which are available as sibling tools.

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

A3.7/5.0
Disambiguation2/5

There are three overlapping ask_pipeworx variants (stable, beta, grounded) plus a dense cluster of six polymarket trading/arbitrage tools, making misselection likely. Several other tools also blur together around research aggregation and entity lookup (entity_profile, compare_entities, recent_changes, validate_claim).

Naming Consistency3/5

All tool names are lowercase and snake_case, but the pattern is inconsistent: some are verb_noun (get_structure, resolve_entity), some are noun phrases (recent_changes, polymarket_edges), and a few are bare verbs (remember, forget, recall). The ask_pipeworx_beta/ask_pipeworx_grounded suffix pattern is readable but not mirrored across the rest of the set.

Tool Count2/5

34 tools is above the 25+ threshold for a server whose stated purpose is Crystallography, and only 3 of those tools actually serve that domain. The rest belong to Pipeworx data lookup, Polymarket betting, memory, research, subscriptions, and unrelated utilities, so the count feels excessive and the scope is unclear.

Completeness3/5

As a broad data-research toolset, there is decent lifecycle coverage: lookup, research, grounded verification, entity resolution, comparison, subscriptions, feedback, and memory all exist. However, for a server named Crystallography the domain surface is thin (search/get/get CIF only), and there is no general web-search fallback for topics not in the structured catalog.

Resources