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Data Providence

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 readOnly, openWorld, idempotent. Description adds that it internally calls 'ai_visibility_check' per entity and ranks results, providing behavioral insight beyond 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?

Two sentences, front-loaded with purpose, then details. Every sentence is informative with no redundancy.

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?

Despite no output schema, description fully covers return values (ranked list with score, confidence, signal density). Inputs, process, and outputs are all described adequately.

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 has 100% coverage, baseline 3. Description adds meaning: entities are brand/business names, first is subject; context disambiguates common names. This adds value over schema alone.

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?

Description clearly states verb 'Compare', resource 'AI visibility across multiple entities', and distinguishes from sibling 'ai_visibility_check' which checks single entities. It specifies output: ranked list with score, confidence, signal density.

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. Implicitly distinguishes from single-entity check, but does not explicitly list when not to use or provide alternative 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

A4.1/5.0
Disambiguation3/5

Most tools have distinct purposes, but several clusters overlap: ask_pipeworx vs ask_pipeworx_beta are currently functionally identical, polymarket_arbitrage / polymarket_edges / polymarket_kalshi_spread all hunt mispricings via different mechanisms, ai_visibility_check is wrapped by scan_competitor_ai_presence, and discover_tools vs suggest_questions both serve tool discovery. The rich descriptions mitigate but do not eliminate misselection risk.

Naming Consistency4/5

Names are all snake_case and follow recognizable conventions: verb_noun for actions (compare_entities, resolve_entity, validate_claim), domain-prefixed families (polymarket_*, pipeworx_*, recent_*, ask_pipeworx_*), and a few bare verbs (remember, recall, query). Minor deviations like bet_research (noun_verb) and noun-only names (datasets, metadata) break the pattern, but the overall scheme is predictable.

Tool Count2/5

At 34 tools, this exceeds the 25+ threshold for 'too many' and bundles several distinct domains — general data querying, prediction markets, AI visibility, memory, subscriptions, open data, and npm auditing — into one server. The breadth is defensible for a data platform, but the agent-facing surface is sprawling and would benefit from splitting into focused servers.

Completeness4/5

The core data workflow is well covered: discover (discover_tools, suggest_questions), resolve (resolve_entity), query (ask_pipeworx), ground (ask_pipeworx_grounded, validate_claim), research (deep_research), compare (compare_entities), profile (entity_profile), and changes (recent_changes). Prediction markets, memory, and subscriptions each have full lifecycles. The main gap is no tool for fetching returned pipeworx:// citation URIs directly, plus a few soft-failing sources.

Resources