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words

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

A4.5/5.0
Behavior4/5

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

Discloses rate limiting (5 per identifier per day), free usage, and the claim_token mechanism for later reads. Annotations are all false and the description adds significant behavioral context beyond them, without contradicting anything.

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?

Longer than typical but every sentence carries operational weight. It is front-loaded with the main purpose and usage triggers, and the rest covers important exclusions, limits, and token behavior. No filler, though slightly verbose.

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 core usage, exclusions, rate limits, and the follow-up token mechanism. Since there is no output schema, it sufficiently explains the return token behavior. The distinction between account vs. no-account filing is only implied, leaving minor ambiguity.

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 value by explaining how to use claim_token (passing it back to read status) and advising not to paste end-user prompts, enriching the meaning of the message and claim_token parameters.

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 purpose: telling the Pipeworx team about something broken, missing, or needing to exist. It distinguishes itself from siblings like ask_pipeworx by focusing on feedback reporting rather than questions, and explicitly scopes to Pipeworx-served tools.

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?

Provides explicit when-to-use guidance: bug, feature/data_gap, praise. Also gives exclusions (tools from other MCP servers) and directs users to file elsewhere. Explains the claim_token flow for checking resolution status.

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

A3.7/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx_beta is currently identical to ask_pipeworx, and find_related with syn/rhy options duplicates find_synonyms and find_rhymes. The six Polymarket tools and four entity-research tools also have fuzzy boundaries, so agents will struggle to reliably pick the right one.

Naming Consistency2/5

Conventions are mixed: most tools follow verb_noun (ask_, find_, compare_, validate_) but several are bare nouns or noun phrases (entity_profile, recent_alertes, recent_changes) and others are bare verbs (remember, forget, subscribe, unsubscribe). The server name 'words' matches only five of 36 tools, adding further confusion about what to expect here.

Tool Count2/5

36 tools is well into the heavy range, and the set bundles word lookups, a universal data router, six Polymarket tools, memory, subscriptions, and meta-utilities under one server. Many of these would be better split into dedicated, purpose-scoped servers.

Completeness4/5

For the dominant data-research and prediction-market scope, the surface is strong: grounded lookups, deep reseach, entity profiles, compareions, change feeds, claim verification, edge scanners, fill-risk checks, subscriptions, and memory are all covered. Minor gaps exist: taking 'words' literally there is no defintion or spelling tool, and there is no generic open-web search, but as a data-research toolkit it is quite complete.

Resources