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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. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations are all false (not read-only, not idempotent, etc.), which gives little safety signal. The description compensates by disclosing important behavior: rate limiting (5/day), quota-free usage, claim_token flow for follow-ups, and that the team reads digests daily. This adds real behavioral context beyond the annotation booleans.

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?

Description is longer than necessary but every sentence serves a unique purpose (usage, exclusions, token flow, rate limits). It's front-loaded with the main action and then progressively details edge cases. Could be trimmed slightly without losing value, but not overly verbose for the complexity.

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 4 params, 1 enum, nested objects, and no output schema, the description covers all critical aspects: what to file, when, when not, exclusions, token mechanism, rate limit, and post-filing follow-up. It even explains the team's review cadence. Nothing important is left unstated.

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 extra meaning around the claim_token usage ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"}) to read whether it was fixed'), which goes beyond the schema. It also clarifies the context object's purpose. Minor deduction because the message and type params are already well-documented in the schema.

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?

Description opens with a clear verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It explicitly enumerates feedback types (bug, feature, data_gap, praise) and differentiates this tool from siblings like ask_pipeworx and pipeworx_trending, which are inquiry/trend tools, not feedback submission.

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, missing tool, praise. Also gives a strong exclusion: do not report issues from other MCP servers, and tells how to identify which server the tool came from. This is exemplary usage guidance with clear boundaries and alternatives.

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
Disambiguation4/5

Most tools have clearly distinct purposes with detailed usage guidance, but the several query entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) overlap conceptually and require careful reading to select correctly. Notably, ask_pipeworx_beta currently behaves identically to ask_pipeworx, which could cause confusion.

Naming Consistency3/5

Tool names mix verb-first (remember, resolve_entity) and noun-first (entity_profile, deep_research) patterns, with some using prefixes like 'polymarket_' or 'ask_pipeworx'. While all are snake_case and readable, the lack of a single consistent convention makes the set feel less coherent than it could be.

Tool Count3/5

33 tools is on the high end for a single server, though the broad domain (data retrieval, prediction markets, memory, subscriptions, web scraping) justifies much of the sprawl. Still, the count borders on heavy, and some tools could potentially be consolidated (e.g., the ask_pipeworx variants).

Completeness4/5

The toolset provides comprehensive coverage for its core data querying and analysis domain, with lifecycle coverage for memory and subscriptions. Minor gaps exist, such as no generic 'fetch page content' tool despite having get_metadata and take_screenshot, but these do not undermine the primary functionality.

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