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Glama

Themeparks

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds context about the returned fields (source, citation_uri, raw payload) and the effect of mark_read flagging events as read. However, it does not fully contradict annotations, but the mark_read behavior might imply a state change, creating a slight inconsistency.

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 at four sentences, each adding essential information. It front-loads the purpose and immediately describes return fields. There is no redundancy or unnecessary detail.

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?

With no output schema, the description adequately explains the return structure (source, citation_uri, payload). It covers key parameters and usage scenarios. However, it could mention default values or error handling, but overall it is complete for a filtered-list tool.

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?

Input schema covers all 5 parameters with descriptions (100% coverage). The description adds value by providing an example for 'type' ('sec_8k') and clarifying that 'since' expects an ISO timestamp. It also explains the behavior of 'mark_read' beyond the schema. This is above the baseline of 3.

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.' It specifies the resource (subscription feed) and the action (pull recent alerts). The description distinguishes it from siblings like 'list_subscriptions' or 'recent_changes' by focusing on fired events from the feed.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for retrieving recent subscription alerts and mentions polling is acceptable, but it does not explicitly state when to use this tool versus alternatives like 'list_subscriptions' or 'recent_changes'. There is no direct comparison or when-not-to-use guidance.

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
Disambiguation3/5

Most tools have distinct, well-documented purposes, but several overlap or are explicitly redundant: ask_pipeworx_beta currently behaves identically to ask_pipeworx, discover_tools and suggest_questions both serve as discovery entry points, and scan_competitor_ai_presence wraps ai_visibility_check. The thematic split between theme-park, data-lookup, prediction-market, and memory tools also forces agents to navigate unrelated clusters.

Naming Consistency3/5

Naming is a mix of verb_noun (list_destinations, get_wait_times, remember, resolve_entity), noun_phrase (entity_profile, recent_changes, bet_research), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Within families the patterns are consistent, but across the set the conventions are inconsistent and sometimes reverse the verb/noun order, making the surface harder to predict.

Tool Count2/5

35 tools is heavy, and the server is named Themeparks yet only 4 tools actually relate to theme parks. The remaining 31 tools span Pipeworx data retrieval, prediction markets, memory, subscriptions, and feedback, creating a bloated and misaligned scope. A tightly scoped theme-park server would need far fewer tools, and a general data research server would not be named Themeparks.

Completeness2/5

For the implied theme-park domain, the surface is thin: list destinations, get entity metadata, get schedule, and get wait times cover basic lookups but omit search, attraction details beyond waits, historical data, pricing, dining/show info, and park updates. The non-theme-park tools are extensive, but they do not complete the server's apparent stated purpose.

Resources