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Valorant

by io.github.pipeworx-io

Recent Alerts

recent_alerts
Read-onlyIdempotent

Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional — filter to one subscription type.
limitNoMax events to return (1-200, default 50).
sinceNoOptional ISO timestamp — return events fired_at >= this time.
mark_readNoFlag the returned events read in the same call (default false).
unread_onlyNoReturn only events where read_at is null (default false).

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds critical behavioral context: the mark_read parameter will flag events as read, affecting future calls ('so the next call only shows newer ones'). It also mentions the GET endpoint alternative, providing additional transparency beyond the 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?

Four sentences, front-loaded with the primary purpose. Every sentence adds unique value: purpose, return contents, filtering, mark_read behavior, and a cross-reference to an alternative access method. No filler or repetition.

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?

For a tool with 5 parameters, no output schema, and moderate annotations, the description explains the return structure, filtering, state-modifying parameter, and polling suitability. It does not list all possible type values or describe the output schema in detail, but given the lack of output schema, the provided fields suffice. The mention of the GET endpoint adds completeness for alternative use cases.

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% (all 5 parameters documented). The description adds meaning by explaining the type filter with an example ('sec_8k'), clarifying that 'since' is an ISO timestamp, and detailing the mark_read behavior. It also notes the return fields ('source, citation_uri... raw event payload'), which complements the schema without repeating it.

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 uses strong verbs ('Pull fired events') and clearly specifies the resource: 'fired events from your subscription feed.' It details what each alert carries (source, citation_uri, raw payload), making the tool's purpose unambiguous. Among siblings, this is the only alert retrieval tool, so no confusion.

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 explains usage context: polls work fine, and mentions an alternative GET endpoint for scripts/dashboards. It specifies filtering by type and since, and explains the mark_read flag's effect on subsequent calls. However, it does not explicitly state when not to use this tool or compare it directly to siblings, although siblings are diverse.

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