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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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: how mark_read affects subsequent calls ('only shows newer ones'), that events carry specific fields, and that polls work fine. 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.

Conciseness5/5

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

The description is a concise paragraph with no wasted words. It front-loads the primary purpose and efficiently covers key features in three sentences.

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 no output schema, the description adequately explains the return fields and key behaviors like mark_read and filtering. It does not detail pagination or ordering, but for a read-only feed tool with 5 optional parameters, it provides sufficient context for an agent to use it 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%, but the description adds meaningful examples (e.g., 'sec_8k' for type) and clarifies behavior (mark_read flagging read events, since expects ISO timestamp). Limit and unread_only are not further elaborated, but overall the description enhances parameter understanding.

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 starts with 'Pull fired events from your subscription feed,' clearly stating the verb (pull) and resource (events). It specifies what is returned (source, citation_uri, raw payload), distinguishing it from sibling tools like query or search_within.

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 context on filtering by type and since, and notes that polling works fine. It also mentions an alternative REST endpoint for scripts/dashboards. However, it does not explicitly state when not to use this tool versus others, nor mention any prerequisites or limitations.

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

Several tool pairs have blurred boundaries: 'ask_pipeworx', 'ask_pipeworx_beta', and 'ask_pipeworx_grounded' serve overlapping routing purposes with minor differences in grounding or versioning, which can confuse an agent. Similarly, 'pipeworx_feedback' and the user feedback mechanism inside other tools lack clear tool-level distinction. Most other tools are distinct but the cluster of ask_pipeworx variants lowers overall clarity.

Naming Consistency4/5

Tool names largely follow a consistent verb_noun or prefix_noun pattern (e.g., ask_pipeworx, resolve_entity, scan_dependency). Some names like 'bet_research' and 'datasets' deviate from this pattern but remain readable. No chaotic mixing of conventions like camelCase and snake_case is present, so consistency is high overall.

Tool Count3/5

With 34 tools, the count is on the higher side for a single server, yet the tool set covers a broad domain of data access, analysis, and monitoring (data pipelines, prediction markets, compliance scans). Given the variety of distinct capabilities offered, 34 is borderline but not excessive enough to drop to a 2, as each tool addresses a concrete need.

Completeness5/5

The tool set offers a remarkably complete lifecycle for data operations: discovery (suggest_questions, discover_tools, datasets), entity resolution (resolve_entity, metadata), querying and retrieval (ask_pipeworx, deep_research, query), analysis and comparison (compare_entities, entity_profile, validate_claim), memory (remember, recall, forget), monitoring (subscribe, recent_alerts, polymarket_edge_tracker), and feedback (pipeworx_feedback). Niche tools like bet_research, scan_dependency, and generate_llms_txt further fill domain-specific gaps. No obvious missing operations for the stated purpose.

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