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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 inherent signal, but the description richly compensates by disclosing rate limiting ('Rate-limited to 5 per identifier per day'), zero quota impact, daily team reading cadence, roadmap influence, and the claim_token lifecycle (returns a token, can be used later to check resolution). No contradictions with annotations exist.

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?

The description is front-loaded with the core purpose, then logically progresses through use cases, boundary conditions, message guidelines, claim-token flow, impact, rate limits, and cost. Every sentence contributes distinct, non-redundant information; there is no filler or repetition.

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?

With no output schema, the description effectively explains the claim_token response and follow-up flow, as well as rate limits and cost. A minor gap is that it states the no-account case returns a claim_token but does not explicitly describe what happens when an account is present, though this is unlikely to confuse an agent. Overall, it is sufficiently complete for the tool's simplicity.

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 provides 100% coverage for all four parameters with detailed descriptions, so the baseline is 3. The description adds workflow-level context: it clarifies the 'message' parameter with formatting rules (reference Pipeworx tools, not user prompts), explains 'type' through real-world scenarios, and offers a concrete example of 'claim_token' usage ('pipeworx_feedback({claim_token:"pwfb_…"})'). This exceeds the schema's literal explanation.

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 pairing. It then enumerates concrete use cases (bug, feature/data_gap, praise) and explicitly differentiates the tool from other MCP servers' feedback channels, making its purpose unmistakable.

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?

It gives explicit when-to-use guidance ('Use when a tool returns wrong/stale data (bug)...') and when-not-to-use ('if the tool came from a different MCP server... file it with that server instead'). It also provides content-formatting instructions ('Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt') and the claim_token follow-up workflow, making this a complete usage guide.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct, well-described purposes, but there is some overlap, especially among prediction market tools (bet_research, polymarket_arbitrage, etc.) and between ask_pipeworx and ask_pipeworx_grounded. Agents might occasionally select the wrong tool without careful reading.

Naming Consistency3/5

Tool names follow a mix of snake_case and camelCase (e.g., ai_visibility_check vs discover_tools). Some names are descriptive but inconsistent in style (subscribe, unsubscribe, list_subscriptions). Pattern is not uniform.

Tool Count3/5

With 32 tools, the server covers many domains (news, financials, prediction markets, entity resolution, memory). While each tool has a justification, the count feels heavy for a single server, and some tools could be consolidated.

Completeness3/5

The tool set spans a wide range of data sources and operations, but there are notable gaps. For news, only search and top headlines exist without advanced filtering. Prediction markets lack order placement tools. The broad scope means depth is sacrificed in some areas.

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