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dictionary

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 the annotations. It states that setting mark_read:true flags events as read and affects future calls, which is a state-changing side effect. This conflicts with readOnlyHint:true and also undermines idempotentHint, as repeated calls could behave differently. This is 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.

Conciseness5/5

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

The description is compact yet densely informative. It starts with a clear action phrase, lists return fields, explains filtering, mark_read, polling, and alternative access—all in one flowing but structured sentence. Every clause earns its place, and there is no redundant content.

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?

The description is comprehensive for a read-focused tool with 5 optional parameters and no output schema. It covers the return payload (source, citation_uri, raw event), filtering by type and since, mark_read side effect, polling suitability, and an alternative endpoint. The only missing details (limit, unread_only) are adequately documented in the schema. The tool is fully understandable from the description alone.

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?

The schema already covers all parameters with descriptions (100% coverage), but the description adds valuable semantic detail beyond the schema. It gives a concrete type example ('sec_8k'), clarifies the 'since' parameter as an ISO timestamp, and explains the behavioral effect of mark_read. This adds meaning beyond the raw schema.

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 the tool's function with a specific verb ('Pull') and resource ('fired events from your subscription feed'). It distinguishes itself by focusing on recent alerts and mentions return payload details, making it clear what this tool does.

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?

Provides clear usage context: explains filters, mark_read behavior for incremental reads, notes that polling is fine, and offers an alternative URL for scripts/dashboards. However, it does not explicitly mention when to prefer this over sibling tools, so no exclusions are given.

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

A3.9/5.0
Disambiguation2/5

Several tools have near-identical purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded are the same router with only an evidence-extraction difference. polymarket_edges, bet_research, and polymarket_arbitrage also overlap heavily in surfacing mispricings, and ai_visibility_check is essentially a single-entity version of scan_competitor_ai_presence.

Naming Consistency3/5

There are recognizable patterns: ask_pipeworx_*, polymarket_*, verb_noun pairs like define_word, get_synonyms, resolve_entity. However, conventions are mixed across the set — ask_pipeworx_beta uses a suffix, ai_visibility_check vs scan_competitor_ai_presence are phrased in different styles, and memory tools (remember/recall/forget) follow yet another pattern. Readable but not predictable.

Tool Count2/5

33 tools is heavy for a single server, and many of them are meta-tools (discover_tools, suggest_questions, ask_pipeworx variants, pipeworx_trending, pipeworx_feedback, memory tools) that inflate the surface. The count would be defensible if each tool were orthogonal, but the overlap in research/Polymarket/memory areas means several tools do not earn their place.

Completeness4/5

For the actual Pipeworx data-access domain, coverage is quite rich: lookup, grounded answers, deep research, entity profiles, comparisons, claim validation, subscriptions, memory, and discovery tools all exist. Minor gaps remain (e.g., no tool to manage account/API keys, patents soft-fail), but the core query-research-monitor lifecycle is well covered. The server name 'dictionary' is misleading — only two tools serve a dictionary purpose — yet the inferred domain from descriptions is a data gateway, for which the surface is strong.

Resources