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Data Austin

by io.github.pipeworx-io
OverviewAdmin

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

A4.9/5.0
Behavior5/5

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

Annotations are all false, providing minimal safety cues, but the description compensates with rich behavioral details: rate-limiting ('5 per identifier per day'), quota implications ('doesn't count against your tool-call quota'), the claim_token retrieval flow, and team SLA ('reads digests daily'). These are exactly the non-obvious behaviors an agent needs to understand, going far beyond what the annotations state.

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?

Every sentence in the description serves a distinct purpose: definition, usage, exclusions, formatting rules, token workflow, and operational details. There is no filler or redundancy, and the most critical info (what it does and when to use) appears first. Despite its length, each clause earns its place given the tool's multi-faceted behavior.

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 description accounts for all relevant operational aspects: success/failure paths (claim_token generation), retry semantics (passing token later), rate limits, quota impact, response timeframe (daily digest), and scope boundaries. Since there is no output schema, the description fully substitutes for return-value documentation, making it complete for this tool's complexity.

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 covers all parameters (100% coverage), the description adds meaningful usage context: instructing to 'describe the issue in terms of Pipeworx tools/packs' clarifies the message parameter, and 'pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})' enriches the claim_token semantics beyond the schema's terse definition. This pushes it above the baseline 3.

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 specific verb+resource pairing ('Tell the Pipeworx team') and clearly enumerates the feedback types (bug, feature, data_gap, praise). It further distinguishes itself from siblings by limiting scope to 'Pipeworx tools' and explicitly excluding feedback about other MCP servers, making its unique role 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?

The description gives explicit when-to-use triggers ('returns wrong/stale data', 'wish existed', 'worked surprisingly well') and a direct when-not-to-use exclusion ('if the tool came from a different MCP server... file it with that server instead'). It also clarifies ambiguity by referencing the Pipeworx tool list as the authoritative scope, fulfilling all criteria for usage 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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