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Movies

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds valuable behavioral detail: the mark_read flag mutates read state (affecting future calls), and the feed is persisted and also accessible via a public URL. This goes beyond the annotation basics without contradicting them.

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 remarkably concise at three sentences, front-loaded with the primary action. Every sentence conveys essential information: what it returns, how to filter, the read-flag side effect, and an alternative access method. There is no redundancy or filler.

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?

Given five optional parameters and no output schema, the description covers the tool's return structure (source, citation_uri, raw payload), filtering, and read-state behavior. It does not address pagination, error handling, or default ordering beyond 'most recent', but for a modest polling tool this is adequate and no critical gaps are evident.

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%, so parameters are already documented. The description adds concrete context: a 'type' example ('sec_8k'), clarifies 'since' as ISO timestamp, and explains the effect of mark_read. This enrichment provides meaning beyond the schema's field 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 states a specific verb ('Pull') and resource ('fired events from your subscription feed'), making the tool's function immediately clear. It further specifies the return payload (source, citation_uri, raw event payload), distinguishing it from sibling tools like list_subscriptions or recent_changes which operate on different data.

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 gives clear usage context, including how filtering works and that polling is supported. It also mentions an alternative REST endpoint for scripts/dashboards, which is pertinent guidance. However, it does not explicitly contrast with sibling tools or state when not to use this tool, 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

A3.5/5.0
Disambiguation2/5

Many tools have heavily overlapping purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, while entity_profile, compare_entities, and recent_changes all pull overlapping company data. The server is named 'Movies' but only 4 of 35 tools relate to movies, making it impossible to infer what the tool set is actually for.

Naming Consistency2/5

Naming is a mix of verb_noun (search_movies, get_tv_schedule), bare nouns (remember, recall), brand prefixes (pipeworx_*, polymarket_*), and ad-hoc verbs (ask_pipeworx vs validate_claim). There is no consistent convention; even the pipeworx family uses ask_ vs grounded vs beta suffixes that don't follow a predictable pattern.

Tool Count2/5

35 tools is heavy for a single server, and for a 'Movies' server it is extreme overkill since the vast majority have nothing to do with movies. Even if the intent was a general data/betting server, 35 tools exceed the upper bound of the well-scoped range and would be better split into focused servers.

Completeness2/5

As a movies/TV server it is severely incomplete: there is no get_movie, no reviews, no watchlist, no person/actor search—only search_movies, search_tv_shows, get_tv_show, and get_tv_schedule. If instead the domain is Pipeworx data, coverage is better but still lacks mutation tools (e.g., no create/update for subscriptions beyond subscribe/unsubscribe) and the movie tools become dead weight.

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