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Glama

Mockdata

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

A3.8/5.0
Behavior1/5

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

The description discloses mark_read side effects and the persisted feed behavior, but these contradict idempotentHint and readOnlyHint annotations. If events are flagged read, repeated calls are not idempotent, and the operation is not purely read-only.

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?

Three sentences, each adding value: what it returns, how to filter, mark_read side effect, and alternative access. No 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?

Covers return format, filtering, side effects, and alternative access. No output schema exists, so explaining the payload is useful. Lacks error conditions or pagination, but for a simple feed reader it's sufficient.

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 covers 100% of parameters. The description adds semantics for 'type' (example sec_8k), 'since' (ISO timestamp), mark_read (flag read), and unread_only (read_at null), enriching the schema definitions. Limit is not mentioned but schema covers it.

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 names a specific resource ('fired events from your subscription feed') and a clear action ('Pull'), with return details (source, citation_uri, payload). This distinguishes it from siblings like list_subscriptions or 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?

Gives concrete usage context: filters by type/since, mark_read behavior, and polling suitability. Also points to an HTTP endpoint for scripts/dashboards. No explicit exclusions or mention of sibling tools.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is overlap within the ask_pipeworx family (beta, grounded) and Polymarket tools (arbitrage, edges, fill_risk), which could cause confusion. Detailed descriptions mitigate this, but the boundaries are not always clear.

Naming Consistency3/5

Tool names use snake_case but lack a consistent verb_noun pattern. Some are imperative (discover_tools), others are descriptive (ask_pipeworx, bet_research), and some are noun phrases (entity_profile, recent_alerts). This inconsistency makes it harder to predict tool names.

Tool Count3/5

33 tools is on the higher side for a single server, but the broad scope (company data, prediction markets, memory, etc.) partially justifies the count. However, many tools are variations of core functionality, suggesting possible consolidation.

Completeness4/5

The tool set covers a wide range of data sources and tasks, including company profiles, comparisons, economic data, and prediction markets. The universal ask_pipeworx router fills most gaps, though some niche data sources might not be directly accessible.

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