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Open Sanctions

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?

Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and non-destructive. The description adds valuable behavioral detail: probes each entity with ai_visibility_check, ranks by score, and returns a ranked list. No contradictions.

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 4 sentences, front-loaded with the main action, then process, then use case. No unnecessary words; every sentence adds value.

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?

For a tool with 4 parameters and no output schema, the description covers all critical aspects: purpose, process, usage, and return value (ranked list with score, confidence, signal density). It is 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 meaning by explaining that the first entity is treated as subject and that it probes each entity, which clarifies the 'entities' and 'context' parameters' roles. Also mentions models and API key in context.

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 ai_visibility_check and ranking by score. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (likely different comparison).

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?

Explicitly states use case: competitive AI-marketing audits, with an example question. Provides clear context but does not explicitly state when not to use or list alternatives, though sibling tool names imply different scopes.

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

A3.6/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, especially the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research) which all serve to query data but with subtle differences. An agent would struggle to distinguish which to use without deep understanding of their nuances.

Naming Consistency3/5

Most tool names follow a verb_noun or noun_noun pattern in snake_case, but there are exceptions like 'forget', 'recall', 'remember' which are single verbs, and names like 'ask_pipeworx' mix verb and proper noun. Overall pattern is discernible but not uniform.

Tool Count2/5

33 tools is high for a server named 'Open Sanctions' which implies a focused domain. The tool set spans sanctions, meta-querying, prediction markets, memory, subscriptions, and more, making it feel bloated and unfocused relative to the server's name.

Completeness2/5

For a sanctions server, only two tools (search_entities, get_entity) directly address the domain, missing obvious operations like update, delete, or list. Many tools are unrelated to sanctions, leaving the core domain incomplete.

Resources