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lorem

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. Added

TDQS

A3.8/5.0
Behavior1/5

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

The description explicitly states that setting mark_read:true flags returned events as read, which modifies the feed state. This directly contradicts the annotation readOnlyHint: true, which claims the tool is read-only. Per rubric, any such contradiction mandates a score of 1 and an annotation contradiction flag.

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 single cohesive paragraph with no fluff. It leads with the action, then return value, filtering, mark_read semantics, and then the HTTP alternative. Every sentence earns its place and the structure is logically ordered and appropriately sized.

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 5-parameter tool with no output schema, the description covers the return payload, filtering, mark_read state changes, polling suitability, and a mirror endpoint. It relies on the schema for limit/unread_only but gives enough context for correct usage. The annotation contradiction is a notable inconsistency, but the description itself is fairly complete, so a 4 is appropriate.

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?

The schema covers all 5 parameters with descriptions (100% coverage), so the baseline is 3. The description adds valuable context by giving an example for 'type' (sec_8k), clarifying 'since' as an ISO timestamp, and detailing how 'mark_read' affects subsequent calls. This added meaning justifies a score of 4.

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 opens with 'Pull fired events from your subscription feed', a specific verb and resource, clearly identifying the tool as an alert/event retriever. It further specifies the return contents (source, citation_uri, raw payload) and filter capabilities, distinguishing it from siblings like list_subscriptions and recent_changes.

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 clear usage context: it mentions polling works, gives an HTTP alternative (GET registry.pipeworx.io/alerts.json) for scripts/dashboards, and explains filtering by type/since and mark_read for read tracking. However, it does not explicitly state when to avoid this tool in favor of siblings, so it falls short of a 5.

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

A3.6/5.0
Disambiguation1/5

Multiple tools have nearly identical purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all handle natural-language data queries, with ask_pipeworx_beta explicitly duplicating ask_pipeworx. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research) also heavily overlaps, and ai_visibility_check is a single-entity version of scan_competitor_ai_presence. An agent would frequently be unable to tell which tool to select.

Naming Consistency2/5

All names are snake_case, but the pattern is inconsistent: some are verb_noun (generate_llms_txt, resolve_entity), some are bare verbs (forget, recall, subscribe), and several are noun-first domain names (polymarket_edges, pipeworx_trending, entity_profile). There is no uniform verb convention, and the mix makes it hard to predict what a tool does from its name.

Tool Count2/5

At 33 tools, this is well above the 'heavy' threshold and includes several near-duplicates: three ask_pipeworx variants and six polymarket_* tools. While the underlying platform is broad, this meta-layer could be consolidated to 15-20 tools without losing capability.

Completeness4/5

The core data-query workflow is well covered: ask, deep research, entity profile, compare, validate, resolve ID, and search inside documents. The memory lifecycle (remember/recall/forget) and subscription lifecycle (subscribe/list/recent_alerts/unsubscribe) are also complete. However, the set includes unrelated utilities (generate_paragraphs, scan_dependency, generate_llms_txt) that don't belong to the main data domain, and there is no direct tool to execute a raw discovered tool by name.

Resources