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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.

TDQS

A4.8/5.0
Behavior5/5

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

With all annotations false, the description carries the full behavioral burden and delivers richly: rate limit (5/day), free/no quota, daily digest read, returns claim_token without account, token can be used later to check status/resolution. No contradiction with annotations.

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 long but every sentence adds practical value: purpose, usage triggers, exclusions, message guidance, token mechanism, rate limit. It is front-loaded with the core purpose and well-organized, though slightly verbose.

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?

For a feedback tool with no output schema, the description fully explains return behavior (claim_token), resolution status, and roadmap impact. It covers all necessary aspects: when, how, exclusions, limits, and follow-up, making it complete for agent use.

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, but the description adds meaningful usage context: claim_token must be used alone with no other arguments, message should reference Pipeworx tools/packs and not paste end-user's prompt, and context is optional structured info. This goes beyond the schema's descriptions.

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 explicitly states the tool's purpose with a specific verb ('Tell the Pipeworx team') and resource, and clearly distinguishes it from sibling tools by focusing on feedback about Pipeworx tools rather than other MCP servers or research/query 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 scenarios (bug, feature, data_gap, praise) and when-not-to-use (different MCP server, with specific instruction to file elsewhere). Also gives follow-up usage via claim_token and guidance on what not to include (end-user's prompt).

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

Tools are a mix of art-related and Pipeworx data tools, with no clear boundary. Many Pipeworx tools overlap in functionality (e.g., ask_pipeworx, deep_research, bet_research), causing confusion. Art tools are distinct but sparse.

Naming Consistency2/5

Tool names mostly use snake_case but follow inconsistent patterns: some are verb-based (ask_pipeworx, search_artworks), others are noun-based (entity_profile, pipeworx_feedback). There is no unified convention across the server.

Tool Count1/5

33 tools is excessive for an art-focused server; only 4 tools (search_artworks, get_artwork, get_departments, generate_llms_txt) relate to art. The rest are from a different domain (Pipeworx), creating a severe scope mismatch.

Completeness2/5

For the art domain, only basic search and retrieval are provided; missing browsing, filtering, or creation tools. The Pipeworx tools are comprehensive but irrelevant to the server's stated purpose, leaving the art surface incomplete.

Resources