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Science Feeds

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds behavioral context: it probes each entity with ai_visibility_check, ranks by score, and surfaces most/least recognized. No contradiction 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?

Two concise sentences. First sentence defines the core function; second adds a use case and output details. No redundancy, front-loaded with key action.

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?

Given 4 parameters (all documented in schema), no output schema, and no nested objects, the description covers the tool's purpose, process, and return characteristics (ranked list with score, confidence, signal density). It is sufficient for an agent to select 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%, baseline 3. The description adds value by explaining the 'entities' parameter: first entry is the subject, rest competitors. It also notes 'context' disambiguates common names, enriching semantic understanding beyond schema 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 tool compares AI visibility across multiple entities side-by-side, using specific verbs like 'probes', 'ranks', and 'surfaces'. It distinguishes itself from sibling tools like ai_visibility_check (single entity) and compare_entities (generic comparison) by focusing on AI visibility and 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?

The description provides a concrete use case: competitive AI-marketing audits, with an example question. It implies when to use (for comparisons) and hints at alternatives (single-entity check via ai_visibility_check), though it could be more explicit about when not to use or specify alternatives.

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

A4/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta (currently identical in behavior), and ask_pipeworx_grounded overlap heavily, and the six-tool Polymarket cluster (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread, bet_research) requires careful reading to distinguish. Descriptions are detailed, but several tools present real selection ambiguity.

Naming Consistency4/5

Nearly all tools use snake_case with a mostly verb-first or resource-first pattern (ask_, list_, fetch_, read_, subscribe, validate_claim). Minor deviations like entity_profile and recent_changes break the verb-noun pattern slightly, but the overall naming is predictable.

Tool Count2/5

34 tools is excessive for the 'Science Feeds' name, which implies a narrow feed-reading service; only 3 tools actually relate to feeds. The rest form a broad Pipeworx grab bag (memory, npm scanning, AI visibility, prediction markets, feedback), making the set feel unfocused and overweight.

Completeness4/5

For the broad query/research domain the descriptions actually establish, coverage is strong: discovery, single lookups, grounded/refusal-safe answers, deep research, entity resolution, comparison, change feeds, claim validation, subscriptions, memory, and feedback are all present. The literal science-feed surface is thin, but the toolkit as a whole has few dead ends.

Resources