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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

A5/5.0
Behavior5/5

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

Discloses the claim_token flow for anonymous filing, the ability to later query resolution status, rate limiting (5/day), and the fact that it is free with no quota impact. All annotations are false, so no contradiction; description adds significant behavioral context.

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?

Long but every sentence earns its place. Front-loaded with the core action, then use cases, exclusions, mechanics, and limits. Structured in a logical flow without redundancy.

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?

Even with no output schema, the description covers all necessary context: return behavior (claim_token), rate limits, message length expectations, and the feedback lifecycle. No gaps remain for an agent to use this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 100% of parameters with descriptions, but the description adds functional meaning: it explains how claim_token works and how to pass it, and instructs users to frame issues in terms of Pipeworx tools/packs, which maps directly to the context object fields.

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 specific verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes from sibling tools by scoping to this connection's tools, not generic querying or research tasks.

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 guidance (bug, feature/data_gap, praise) and an explicit when-not-to-use exclusion for tools from other MCP servers, even naming the alternative action (file with that server). This is textbook 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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TDQS

A3.9/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and suggest_questions all route questions to the same underlying data catalog, making it hard to pick the right one. The Polymarket-related tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) also have overlapping discovery and analysis purposes.

Naming Consistency3/5

Many tools use descriptive snake_case, and the ask_pipeworx family shares a clear prefix, but the set mixes generic memory verbs (remember, recall, forget), brand-prefixed tools (here_*, pipeworx_*), and standalone names like bet_research and scan_dependency. There is no consistent verb_noun pattern across the whole server.

Tool Count2/5

35 tools is heavy for a single MCP server, and a large portion are meta-tools layered over the same 5,752-tool catalog (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad intentional scope explains the count, but the tool surface feels bloated and harder to navigate than it needs to be.

Completeness4/5

The domain is unusually broad—data querying, entity resolution, comparison, monitoring, memory, geolocation, prediction markets, dependency scanning—and the set covers most workflows end to end. Minor gaps exist, like no direct pipeworx:// citation fetcher and no update/list/delete pattern for entity profiles, but the core user journeys are well supported.

Resources