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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).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by explaining the mark_read side effect (flags events as read, affecting future calls) and listing return fields (source, citation_uri, raw payload). 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 concise and front-loaded: opening with the core purpose, then detailing return structure and key parameters. Every sentence adds value, and the length is appropriate for the tool's complexity.

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?

With 5 optional parameters and no output schema, the description covers purpose, parameter semantics, return fields, and alternative access. It provides sufficient context for an AI agent to select and invoke the tool 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%, providing baseline parameter descriptions. The description enhances understanding with concrete examples (e.g., 'sec_8k') and clarifies the effect of mark_read on subsequent calls, adding meaning beyond the schema.

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 pulls fired events from a subscription feed, using specific verbs and resources. It distinguishes itself from sibling tools like fetch_feed and read_feed by focusing on 'recent alerts' and mentioning an alternative HTTP endpoint for scripts/dashboards.

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 explicit guidance: polls work fine and the same data is available via an HTTP endpoint for scripts. However, it does not explicitly differentiate from other sibling tools like fetch_feed, missing a clear when-not or alternative scenario.

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

Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants, bet_research vs. polymarket_edges, and several entity/profile tools. An agent could easily misselect between similar tools despite detailed descriptions.

Naming Consistency2/5

Tool names are inconsistent: some use snake_case (ask_pipeworx), some camelCase (ai_visibility_check), and verbs vary (ask, bet, compare, scan, validate). No clear naming convention is followed across the set.

Tool Count3/5

With 34 tools, the server covers a broad range of functionalities for a platform like Pipeworx. While each tool seems justified, the count is on the higher end, potentially overwhelming for simpler use cases.

Completeness4/5

The tool set covers data retrieval, memory, subscriptions, Polymarket analysis, and utility functions comprehensively. Minor gaps exist (e.g., no direct user management) but core workflows are well-supported.

Resources