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Glama

Vat

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

A4.3/5.0
Behavior4/5

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

The description clearly discloses the mark_read side effect and what return fields to expect, adding value beyond annotations. However, the mark_read write behavior slightly contradicts the readOnlyHint annotation, though the description transparently addresses it.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, efficiently covering purpose, return fields, filtering, side effects, and alternative access. It is front-loaded and concise, though a bit dense.

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 the absence of an output schema, the description covers return fields and parameter usage well. Annotations provide safety cues. The tool is relatively simple, and the description adequately prepares the agent for invocation.

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?

With 100% schema coverage, the description adds meaningful context: explaining the purpose of each parameter (e.g., 'ISO timestamp', 'Flag the returned events read') and providing an example for type ('sec_8k'). This enhances understanding beyond the schema alone.

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 'Pull fired events from your subscription feed' with a specific verb and resource. It details the return fields (source, citation_uri, raw payload) and distinguishes itself from sibling tools like list_subscriptions by focusing on actual alerts rather than subscriptions.

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 explicit context: 'Poll works fine' and mentions an alternative access method (GET endpoint). It also explains filtering by type and since. However, it does not explicitly state when not to use this tool versus alternatives like recent_changes.

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

A3.7/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying catalog, and ask_pipeworx_beta is currently identical to ask_pipeworx. ai_visibility_check and scan_competitor_ai_presence also overlap, and entity_profile vs recent_changes cover similar ground. The descriptions are detailed, but an agent would frequently struggle to pick the right tool.

Naming Consistency3/5

All names use snake_case, but the pattern is inconsistent: some are verb-first (validate_vat, list_vat_formats, resolve_entity), some are noun-first (entity_profile, polymarket_edges, recent_alerts), and some are product-branded (ask_pipeworx, pipeworx_feedback). It's readable but does not follow a single predictable verb_noun convention.

Tool Count2/5

33 tools is well beyond the 25+ threshold for a coherent set, especially for a server named 'Vat' where only two tools (list_vat_formats, validate_vat) relate to the apparent purpose. The bulk forms a broad data-research platform that would be more appropriately split into separate VAT and Pipeworx servers.

Completeness4/5

For the actual data-research domain, coverage is strong: discovery, single lookup, grounded answers, deep multi-source research, entity resolution, comparisons, claim verification, memory, subscriptions, and prediction-market tooling are all present. The only clear gap is that the VAT-specific surface is minimal (format validation only, no registration/VIES check), but the overall functional surface is otherwise quite complete.

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