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Glama

Ror

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

A3.9/5.0
Behavior1/5

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

The description directly contradicts annotations: readOnlyHint=true while mark_read:true explicitly flags events as read, a state mutation that affects future calls. Similarly, idempotentHint=true conflicts with the described non-idempotent behavior when mark_read is used. This is a serious annotation contradiction.

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 packed with essential information but remains well-structured: core action, return fields, filtering/options, behavioral note, and alternative endpoint. Every sentence earns its place without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description appropriately explains the return payload and key behaviors (polling, mark_read, feed persistence). It also documents the alternative HTTP GET endpoint. This is complete for a polling tool with five parameters.

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 has 100% parameter coverage, so baseline is 3. The description adds value with a concrete type example ('sec_8k'), clarifies 'since' as ISO timestamp, and explains the semantic consequence of mark_read ('next call only shows newer ones'), going beyond the schema's 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'), and details the return payload (source, citation_uri, raw event payload). This distinguishes it from siblings like 'recent_changes' by focusing on the persisted feed written by the evaluator.

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?

Clear usage context is provided: it supports polling, filtering by type/since, and mark_read to control future calls. It also points to an alternative HTTP endpoint for scripts/dashboards, implying this tool is for in-context polling. Explicit comparisons to sibling tools are absent, but the guidance is actionable.

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

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps with ask_pipeworx, and discover_tools/suggest_questions both serve a discovery role. The extremely detailed descriptions help, but the boundaries between meta-tools and research tools are genuinely confusing, especially with 34 tools in one namespace.

Naming Consistency2/5

Naming is a mix of bare verbs (get, search, recall), nouns (affiliation, entity_profile, recent_changes), and verb_noun phrases (resolve_entity, compare_entities, validate_claim). Some families are consistent (polymarket_*), but overall there is no uniform convention or prefix scheme, making the set feel arbitrary.

Tool Count2/5

34 tools is well above the 25+ threshold for 'too many.' While the broad data-platform scope explains some of the count, many tools are meta-utilities (feedback, trending, memory, subscription management) and there are near-duplicate variants (three ask_pipeworx forms, six Polymarket tools) that inflate the surface.

Completeness4/5

For a data-research platform the surface is impressively complete: lookup, grounded verification, entity profiles, comparisons, claim checking, subscription lifecycle, memory, and tool discovery are all covered. Minor gaps exist (e.g., no direct general-purpose web fetch, and ROR lacks create/update, which is acceptable for a curated registry), but agents should rarely hit dead ends.

Resources