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Glama

Wikidata

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

A3.7/5.0
Behavior1/5

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

The description contradicts the readOnlyHint=true annotation by describing mark_read:true which flags events as read, modifying state. Annotations indicate no state modification, but description reveals side effects, a serious inconsistency.

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 a single, efficient paragraph that front-loads the main purpose. It covers key aspects like return structure, filtering, and mark_read without unnecessary words. Could be slightly more structured, but it's concise enough.

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 no output schema, the description explains return values (source, citation_uri, raw payload). It explains all parameters and side effects like mark_read. However, the contradiction with annotations reduces reliability, but the description itself is fairly complete for the tool's purpose.

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%, baseline 3. The description adds meaningful context: explains that 'type' filters by subscription type (e.g. 'sec_8k'), 'since' is an ISO timestamp, and 'mark_read' behavior. This goes beyond schema definitions.

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 it pulls fired events from a subscription feed, with specific verb 'Pull' and resource 'fired events'. It differentiates from siblings by its specific focus on recent alerts from a subscription feed, which is distinct from other tools like list_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 context on when to use (pulling recent alerts from subscription feed), mentions alternatives (the same feed via HTTP endpoint for scripts/dashboards), and notes that polling works fine. It could be improved by explicitly stating when not to use, but the guidance is clear.

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

The tool set is extremely heterogeneous and overlapping. Multiple tools serve nearly identical purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions), and entity-related tools (get_entity, get_wikidata_facts, entity_profile, compare_entities, resolve_entity) have unclear boundaries. The server is named Wikidata but only 3 of 34 tools actually relate to Wikidata, causing confusion about which tool is appropriate.

Naming Consistency2/5

Tool names mix multiple conventions: verb_noun (get_entity, search_entities, list_subscriptions), noun phrases (polymarket_edges, entity_profile, pipeworx_feedback), and inconsistent suffixes (_beta, _grounded). The verbs used are vague and not part of a coherent pattern (get, search, ask, discover, resolve, scan, validate, generate, remember, recall). The naming is readable but lacks a predictable system.

Tool Count2/5

34 tools is high for a server with a focused name like Wikidata, and nearly all tools belong to unrelated domains (Pipeworx data routing, Polymarket betting, memory management, subscriptions, AI visibility). The count appears bloated and scattered rather than well-scoped; most tools could be split into separate servers.

Completeness2/5

For a Wikidata server, the coverage is minimal: only search, get by ID, and human-readable facts are present, with no SPARQL query support or property-level access. The majority of tools address Pipeworx's broad data catalog, leaving the advertised Wikidata purpose severely under-served. The surface is complete for a different domain but incomplete for the stated one.

Resources