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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. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral details beyond annotations: it explains that mark_read:true modifies state (flags events as read), which is not obvious from 'idempotentHint=true'. It also describes return fields (source, citation_uri, raw event payload) and confirms poll suitability. 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?

The description is concise (4 sentences) and well-structured. The first sentence states the core purpose. Each subsequent sentence adds a distinct detail (return fields, filters, side effect, alternative access). No unnecessary words or redundancy.

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?

Given the tool has 5 parameters and no output schema, the description covers key aspects: what is returned, filtering, state-changing behavior (mark_read), polling suitability, and an alternative endpoint. This is sufficient for an agent to understand and correctly invoke the tool without guessing.

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 description coverage is 100%, so baseline is 3. The description adds value by providing examples ('e.g. sec_8k' for type) and clarifying the effect of mark_read on subsequent calls. It does not repeat schema details but enriches the understanding of parameter usage.

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's purpose: 'Pull fired events from your subscription feed. Returns the most recent alerts...' It specifies the verb 'pull', the resource 'events from your subscription feed', and the action 'Returns the most recent alerts'. No sibling tools appear to have overlapping functionality, so differentiation is inherent.

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 guidance on when to use this tool, including filtering options ('Filter by type... and/or since'), a side-effect parameter ('Set mark_read:true to flag returned events read so the next call only shows newer ones'), and an alternative access method ('the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards'). It does not explicitly state when not to use it, but the context is clear.

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

A3.9/5.0
Disambiguation3/5

Several clusters overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near variants (beta currently identical), and the prediction-market tools share adjacent territory. The descriptions do delineate most use cases, but an agent could easily confuse the ask_pipeworx variants or pick between polymarket_edges and bet_research.

Naming Consistency2/5

Naming is a mix of domain-prefixed verbs (data360_get_data, pipeworx_feedback), bare verbs (forget, recall, subscribe), and noun phrases (entity_profile, recent_changes, polymarket_edges). The ask_pipeworx family is consistent, but there is no server-wide verb_noun convention and tool names are not predictable from their function.

Tool Count2/5

34 tools is too many for a well-scoped server, and the set spans unrelated areas: data retrieval, prediction markets, memory, subscriptions, npm dependency scanning, and llms.txt generation. While each tool has a purpose, the overall surface feels sprawling rather than focused.

Completeness4/5

The core data/research workflows are well covered: ask/grounded/deep research, entity identity and profiles, comparisons, claim validation, and subscription lifecycle management are all present. Minor gaps exist, such as no explicit raw-record fetch tool and some auxiliary features appearing as one-off utilities, but no major dead ends are apparent.