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Glama

Ipma Pt

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate the tool is read-only, idempotent, and non-destructive. The description adds value by explaining the mark_read side effect (marking events as read) and stating that polling works fine, confirming safe repeated use. 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—three sentences total—with the most critical information (purpose and return data) in the first sentence. Every sentence adds value, no redundancy.

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?

The description covers the key aspects: what is returned, filtering options, mark_read behavior, and availability via HTTP. Although it doesn't detail pagination or limit limits, those are in the schema. With no output schema, the return fields are adequately stated.

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 descriptions cover all parameters (100% coverage), but the description enriches understanding by providing examples ('sec_8k' for type) and clarifying the effect of 'mark_read' for future calls. This goes beyond the schema's basic property descriptions.

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 action ('Pull fired events') and the resource ('from your subscription feed'), specifying exactly what is returned (source, citation_uri, raw payload). This distinguishes it from sibling tools like 'list_subscriptions' which manage subscriptions rather than retrieve events.

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 when to use the tool (to fetch recent alerts) and provides usage context like filtering options and the mark_read flag. It also mentions an alternative access method (HTTP GET endpoint) but does not explicitly exclude usage scenarios or compare to siblings.

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
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research cluster overlaps heavily on question-routing, and the six polymarket_* tools all operate on prediction-market edges and can be confused. The ipma_*, memory, and subscription tools are distinct, but the overlapping clusters create real misselection risk.

Naming Consistency3/5

Snake_case is used throughout and there are clear prefix groups (ipma_*, ask_pipeworx, polymarket_*), but the rest mix verb-first (compare_entities, discover_tools), noun-first (entity_profile, bet_research), and bare verbs (remember, recall, forget). Readable overall, but no consistent verb_noun convention.

Tool Count2/5

36 tools is heavy, and the server name 'Ipma Pt' implies a narrow Portugal-weather service while 31 of the tools belong to a broad Pipeworx data/prediction-market platform. The scope mismatch makes the count feel bloated rather than curated.

Completeness4/5

The dominant Pipeworx domain is well covered: querying, grounded answers, deep research, entity resolution, comparison, claim validation, subscriptions, memory, and tool discovery are all present with few dead ends. Minor gaps exist (e.g., no general web search tool, thin IPMA historical/warning coverage), but agents can work around them.

Resources