Skip to main content
Glama

Public Suffix List

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).

TDQS

A3.5/5.0
Behavior1/5

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

The description contradicts annotations: mark_read:true flags events as read, which is a write operation, but annotations include readOnlyHint=true and idempotentHint=true. This is a clear contradiction.

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 4 sentences, front-loaded with purpose, and efficiently conveys key information. Could be slightly shorter, but no major waste.

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, polling, and alternative endpoint. Lacks mention of limit and unread_only, but those are in schema. Overall adequate for a tool with good annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all parameters. Description adds context for type, since, and mark_read but does not mention limit or unread_only. Baseline 3 is appropriate.

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 the subscription feed, returns recent alerts with specific fields (source, citation_uri, raw payload), and supports filtering. This distinguishes it from siblings like list_subscriptions which list subscriptions, not alerts.

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?

It mentions polling is fine and provides an alternative endpoint for scripts/dashboards, giving context for usage. However, it does not explicitly state when to use this tool vs alternatives like list_subscriptions or subscribe.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.9/5.0
Disambiguation2/5

Multiple tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all query data in similar ways. Prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) are numerous and confusingly similar. Agents will struggle to choose the right tool.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use snake_case like ai_visibility_check, others are generic single words (parse, remember, forget). There is no uniform verb_noun structure, mixing descriptive names (entity_profile) with vague ones (list_version).

Tool Count3/5

35 tools is a large set for a server named 'Public Suffix List', but the actual domain (comprehensive data platform) may justify many tools. However, the count feels heavy for the apparent scope of the server, with many niche prediction market tools.

Completeness4/5

The tool surface covers a wide range: data query, comparison, subscription, memory management, and claim verification. However, there are gaps in data modification (no update/delete for records) and some prediction market features have no direct counterparts. Overall, the set is fairly complete for its data-fetching purpose.