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Glama

chucknorris

by io.github.pipeworx-io

Send Pipeworx Feedback

pipeworx_feedback

Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). ONLY for tools served by this Pipeworx connection — if the tool came from a different MCP server in your client (another vendor's Gmail, Splunk, Slack, etc. connector), we cannot fix it and reporting it here only delays you; file it with that server instead. Not sure? Pipeworx tool names are the ones this connection lists. Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. Filing without an account returns a claim_token; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed and what changed. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNobug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else.
contextNoOptional structured context: which tool, pack, or vertical this relates to.
messageNoYour feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max.
claim_tokenNoRead the reply to a report you filed earlier: pass the `pwfb_…` token that filing returned, with no other arguments. Returns the status and, once resolved, what actually changed.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / claim_token
      Added value: +{
+  "description": "Read the reply to a report you filed earlier: pass the `pwfb_…` token that filing returned, with no other arguments. Returns the status and, once resolved, what actually changed.",
+  "type": "string"
+}
    • removedInput schema / required
      Removed value: -[
-  "type",
-  "message"
-]
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

The description goes beyond the annotations (which are all false) by disclosing rate limits ('Rate-limited to 5 per identifier per day'), the claim_token follow-up mechanism ('Filing without an account returns a claim_token; pass it back later'), and the team's response cadence ('reads digests daily'). It also instructs on appropriate content ('don't paste the end-user's prompt'), which shapes the tool's side-effect profile.

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 dense paragraph that front-loads the core purpose in the first sentence, then branches into use cases, exclusions, claim_token, and rate limits. Every sentence carries information, but the length is somewhat high; it could be tightened into a bulleted structure, so it earns a 4 rather than a 5.

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?

Given the lack of an output schema, the description fills the gap by explaining exactly what the tool returns: a claim_token for anonymous submissions and a status/change report when passing that token back. It also covers operational details like rate limiting and quota impact. The tool is a feedback submission channel, and the description covers all necessary context for an agent to invoke it correctly.

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?

Although the input schema describes all 4 parameters (100% coverage), the description adds meaningful usage guidance: it explains how the claim_token parameter works as a follow-up handle and advises how to structure the message ('Describe the issue in terms of Pipeworx tools/packs'). It also clarifies that claim_token is used 'with no other arguments,' which is not in the 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 opens with 'Tell the Pipeworx team something is broken, missing, or needs to exist,' which is a specific verb+resource: submitting feedback about Pipeworx tools. It enumerates feedback types (bug, feature, data_gap, praise) and explicitly differentiates from sibling MCP tools by limiting scope to 'tools served by this Pipeworx connection.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also gives a when-not-to-use and an alternative: 'if the tool came from a different MCP server... file it with that server instead.'

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