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chucknorris

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

A4.7/5.0
Behavior5/5

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

Beyond the annotations, the description discloses that mark_read:true flags events as read so subsequent calls return only newer events—a side effect not implied by readOnlyHint. It also explains the citation_uri format and raw payload structure, providing rich behavioral context.

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?

Two sentences, front-loaded with the core action, and every clause contributes: return fields, filters, mark_read behavior, polling suitability, and alternative URL. No wasted words.

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?

For a 5-param tool with no output schema, the description covers the return format, filtering options, mark_read side effects, and alternate access. It also implicitly addresses unread_only and limit via the schema while providing enough context for correct invocation.

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%, so baseline is 3. The description adds an example type ('sec_8k') and clarifies the behavioral consequence of mark_read, which exceeds the schema descriptions. This meaningful addition justifies a 4.

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 opens with 'Pull fired events from your subscription feed,' naming the specific verb and resource. It clarifies the return payload (source, citation_uri, raw event payload) and distinguishes itself from sibling tools like list_subscriptions by focusing on alerts from the persisted feed.

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 states 'Polls work fine' and offers an alternative access method via URL for scripts/dashboards, providing context on when to use this tool directly vs. programmatically. However, it does not explicitly position itself against sibling tools or give when-not-to-use guidance.

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, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical query routers, and the five polymarket_* tools all hunt mispricings in subtly different ways. The memory trio (remember/recall/forget) and subscription trio (subscribe/unsubscribe/recent_alerts) are distinct, but the many data-query tools create frequent ambiguity for an agent deciding which one to call.

Naming Consistency2/5

Naming is a mix of verb_noun (list_categories, resolve_entity, validate_claim), bare nouns (entity_profile, random_joke, deep_research), single verbs (forget, recall), and brand-prefixed nouns (pipeworx_feedback, pipeworx_trending). There's no consistent pattern across the set, so an agent cannot predict a tool's name from its function.

Tool Count1/5

35 tools for a server named 'chucknorris' is an extreme mismatch; only 4 tools actually relate to Chuck Norris jokes. The rest form a sprawling collection of data-research, prediction-market, subscription, and memory utilities that have nothing to do with the stated server identity and overwhelm any agent expecting a simple joke API.

Completeness2/5

The Chuck Norris joke subset is complete (random, by-category, search, categories), but the overall server attempts many unrelated domains—structured data queries, prediction-market arb, entity profiles, subscriptions, memory—none of which are clearly scoped or fully coherent. The result is a grab-bag with no single domain that feels finished, and the incongruous inclusion of joke tools adds confusion rather than coverage.

Resources