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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

A4.9/5.0
Behavior5/5

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

Beyond annotations (which say nothing about side effects), the description discloses the claim_token flow, rate limit ('5 per identifier per day'), free tool-call behavior, and daily digest roadmap impact. This is rich behavioral context that annotations do not provide.

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?

Although long, every sentence serves a purpose: purpose, usage, exclusions, token mechanism, rate limits. It is front-loaded with the core purpose and structured so an agent can quickly extract key rules.

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 tool with 4 params, nested objects, and no output schema, the description fully covers the operational context: how to report, what to avoid, the claim_token lifecycle, and even the expected return format ('returns the status and, once resolved, what actually changed').

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 descriptions cover 100% of parameters, but the description adds crucial semantic constraints: claim_token must be used 'with no other arguments', and the conditional behavior of filing without an account. This elevates 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: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It explicitly names the feedback categories (bug, feature/data_gap, praise) and clearly differentiates from sibling research/memory 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 guidance for each feedback type, and equally explicit when-not-to-use: 'ONLY for tools served by this Pipeworx connection… if the tool came from a different MCP server… file it with that server instead.' Also instructs not to paste end-user prompts, adding practical usage context.

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

B3.4/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes, especially ask_pipeworx, ask_pipeworx_beta (explicitly identical right now), ask_pipeworx_grounded, and deep_research. The Polymarket tools are more distinct, but the boundary between Maven search tools and broader discovery tools like search, search_by_coords, discover_tools, and suggest_questions is not always obvious.

Naming Consistency2/5

Naming is a mix of snake_case actions, branded prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*, plus inconsistent patterns like ai_visibility_check vs scan_competitor_ai_presence. Some clusters are internally consistent, but the overall set follows no predictable convention.

Tool Count2/5

35 tools is heavy for a server named 'Maven Central', and only a handful actually relate to Maven artifacts. The rest are Pipeworx research, prediction-market, memory, subscription, and utility tools, making the set feel sprawling rather than purpose-scoped.

Completeness3/5

For the Maven Central domain, search, coordinate lookup, version listing, and latest-version retrieval cover core read-only needs. However, there is no direct artifact metadata/POM/dependency inspection, and the unrelated Pipeworx and Polymarket tools dilute the surface without filling obvious gaps in the stated domain.