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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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations are all false, providing minimal behavioral signal; the description compensates by disclosing rate-limiting ('Rate-limited to 5 per identifier per day'), the claim_token flow for anonymous reports, and that the team reads digests daily and signal affects roadmap. It also clarifies the tool doesn't count against the tool-call quota.

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 long, unbroken paragraph but dense with necessary context; every sentence introduces a new detail (usage categories, exclusion, token flow, rate limit, quota). It is front-loaded with the core purpose, though the multi-clause structure makes it less scannable than a structured list.

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 tool is complex (requires exclusions, token mechanics, rate limits), but the description covers all required context: when to use, how to frame feedback, what the claim_token does, and rate limits. No output schema exists, but the description explains what filing returns (a claim_token) and how to use it.

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 describes all four parameters at 100% coverage, but the description adds usage semantics beyond the field descriptions: it demonstrates the claim_token call syntax ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})') and instructs users to describe issues in terms of Pipeworx tools/packs rather than pasting end-user prompts.

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 a clear imperative 'Tell the Pipeworx team something is broken, missing, or needs to exist,' establishing the tool's purpose as feedback for Pipeworx. It explicitly distinguishes itself from sibling tools by stating it only handles tools from this Pipeworx connection, not connectors from other MCP servers, and names the categories (bug, feature/data_gap, praise).

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 gives explicit when-to-use guidance: 'Use when a tool returns wrong/stale data (bug)...' and also provides an exclusion: if the tool came from a different MCP server, file with that server instead. It further clarifies 'Pipeworx tool names are the ones this connection lists' to disambiguate from siblings.

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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes. ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route to the same 5,564 tools, differing only in grounding behavior. suggest and lyrics both look up music but in different ways; suggest is broader while lyrics is exact. The core tools are distinct, but the multiple pipeworx variants and music tools create ambiguity.

Naming Consistency2/5

Naming is highly inconsistent. Most tools use snake_case (ask_pipeworx, entity_profile, compare_entities), but several use verb phrases (generate_llms_txt, scan_competitor_ai_presence) and some use short nouns (lyrics, suggest). There's no consistent verb_noun pattern; 'ask_pipeworx' variants mix imperative with domain words, and 'recall'/'remember' are verbs without objects.

Tool Count4/5

With 33 tools, the server covers a wide domain (company research, prediction markets, news, weather, lyrics, memory, subscriptions, etc.). While this is many tools, each has a specific purpose and the variety matches the stated 'universal router' / 'thousands of data sources' value prop. It could be trimmed slightly, but the count is justified by the breadth.

Completeness5/5

The tool surface is remarkably complete for its stated purpose: unstructured lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded), deep multi-source research (deep_research), entity profiles, comparisons, change feeds, arbitrage scanning, memory, subscriptions, and even feedback/governance tools. It covers all common patterns in data retrieval and has distinct tools for edge cases, making it hard to find obvious gaps.

Resources