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Valorant

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

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, ensuring safety. Description adds valuable behavioral context: it uses ai_visibility_check as subtool, ranks by score, and returns 'score, confidence, signal density per entity.' 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is three sentences, front-loaded with purpose and mechanism. It efficiently conveys the tool's value and output. Could trim the example quote slightly, but overall very concise.

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 four parameters (one required), no output schema, and rich annotations, description covers purpose, process, output structure, and use case. It mentions subtool and ranking details. Lacks explicit ordering direction (e.g., highest to lowest score), but is largely complete.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. Description mentions the use of ai_visibility_check but does not add parameter-level meaning beyond schema descriptions. Schema already explains 'entities' first entry is subject, which is adequate.

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 tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and surfaces most/least recognized. Distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic comparison) by specifying the AI-presence context and ranking mechanism.

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?

Description provides explicit use case: 'competitive AI-marketing audits: does Claude know about us as well as our competitors?' It implies when to use (multi-entity comparison) and contrasts with single-entity sibling ai_visibility_check. Could be improved by explicitly stating when not to use or naming 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

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly described as currently identical, and ask_pipeworx_grounded shares the same router. ai_visibility_check is internally wrapped by scan_competitor_ai_presence, discover_tools and suggest_questions both claim 'use this FIRST' as onboarding meta-tools, and entity_profile/compare_entities/recent_changes pull overlapping EDGAR/news/patents data. The detailed descriptions help, but the redundancy is structural, not just cosmetic.

Naming Consistency3/5

All names are snake_case and several families are internally consistent (ask_pipeworx_*, polymarket_*, list_*), but the overall set mixes bare verbs (remember, recall, forget, subscribe), verb_noun (get_agent, compare_entities), noun_noun (entity_profile, bet_research, recent_changes), and adjective/compound forms (deep_research, generate_llms_txt, ai_visibility_check) with no dominant convention. Readable, but clearly heterogeneous.

Tool Count2/5

35 tools is above the 25+ 'too many' threshold, and the count is wildly mismatched to the server's stated identity: a server named 'Valorant' has only 4 game-related tools while the other 31 form a sprawling data-research/prediction-market/utility toolkit. Even considered on its own terms, the set includes several redundant meta-tools and unrelated subsystems (npm scanning, llms.txt generation, memory) that feel bolted on.

Completeness3/5

The dominant data-research domain is well covered: discovery, single and grounded queries, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscription monitoring form a mostly complete surface. However, the server's namesake domain is severely shallow — the Valorant tools only expose static reference data with no match/player/esports coverage — and the prediction-market side lacks obvious write-side or position-management operations.

Resources