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Data Nola

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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details like probing each entity, returning ranked list with score, confidence, signal density, and mentions API key requirement for Anthropic model. 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?

Two concise sentences, zero wasted words. Front-loaded with core purpose, then details. Every sentence earns its place.

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?

No output schema exists, but description fully describes return format (ranked list with score, confidence, signal density). Input constraints (2-8 entities), side effects (none, idempotent), and permissions are implied. Complete for a comparison tool.

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% (all 4 parameters have descriptions). Description adds valuable context beyond schema: first entity treated as subject, supported models details, and shared context purpose. Reinforces but does not fully replace 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 it compares AI visibility across multiple entities side-by-side, ranks them, and identifies most/least recognized. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (different type of comparison).

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

Usage Guidelines5/5

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

Explicitly frames usage for competitive AI-marketing audits with an example question. Implies when to use (comparing multiple entities) and when not (single entity check would use ai_visibility_check). Provides clear context.

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

A3.8/5.0
Disambiguation2/5

Several tool boundaries blur: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve overlapping query/research entry points, and ask_pipeworx_beta is currently identical to ask_pipeworx by the server's own description. entity_profile, recent_changes, and compare_entities also cover overlapping company-investigation territory, forcing agents to parse long descriptions to avoid mis-selection.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow a predictable verb-object or domain-prefix pattern (ask_pipeworx_*, polymarket_*, nola_*, subscribe/unsubscribe). Minor deviations like nola_datasets, polymarket_edges, and ai_visibility_check are noun-first rather than verb-first, but the overall convention is still readable and coherent.

Tool Count2/5

34 tools is well above the 15-25 heavy range and includes multiple near-duplicate query modes, four closely related Polymarket analysis tools, and memory/subscription utilities layered on top of the core data-access surface. While the server appears to be a broad data platform, the count is bloated for an agent to navigate efficiently.

Completeness4/5

The tool surface is remarkably broad: discovery, single-answer lookup, grounded verification, deep research, entity resolution, entity profiles, comparisons, claim validation, NOLA querying, prediction-market analytics, memory, subscriptions, and feedback are all covered with few obvious dead ends. The main gap is that the NOLA-specific surface is thin relative to the server name, though nola_query plus nola_datasets provides a flexible escape hatch.

Resources