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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.

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?

Annotations are all false/uninformative, so the description carries the burden. It fully discloses the claim_token round-trip, daily human review, roadmap impact, rate limiting to 5 per identifier per day, free/quota-free execution, and privacy guidance ('don't paste the end-user's prompt'). 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence earns its place: purpose, use cases, exclusions, identification tip, token workflow, human review, rate limit, and quota impact. It is front-loaded with the core action and then layers operational details efficiently.

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 no output schema and no useful annotations, the description thoroughly covers selection criteria, input semantics, claim-token return behavior, how to retrieve resolution status, and what happens after submission. It compensates for structured-field gaps with rich, actionable context.

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?

Even though schema coverage is 100%, the description adds significant meaning beyond the schema: it maps type enum values to concrete scenarios, explains the claim_token follow-up workflow with an exact call shape, and constrains message content to Pipeworx tool/pack terms rather than user prompts. This is far beyond a schema restatement.

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 action ('Tell the Pipeworx team something is broken, missing, or needs to exist') and clearly names the resource (Pipeworx tools/packs). It distinguishes itself from siblings like ask_pipeworx by scoping exclusively to Pipeworx-served tools and explicitly routing other MCP server tools elsewhere.

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 reports, feature/data-gap requests, and praise. It also gives a concrete when-not-to-use: if the tool came from another MCP server, file it there instead, and even tells the agent how to disambiguate (Pipeworx tool names are the ones this connection lists).

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 are near-identical: ask_pipeworx and ask_pipeworx_beta are explicitly the same function, ask_pipeworx_grounded is the same router with a different response mode, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk all overlap on prediction-market edge detection. search vs search_within vs discover_tools also blur discovery boundaries. An agent would struggle to pick the right tool without reading every long description.

Naming Consistency3/5

All names are snake_case and individually readable, so there's no chaotic style mixing. However, the pattern is inconsistent: bare verbs (search, recall, forget, subscribe), verb_noun (get_package, resolve_entity, scan_dependency), noun phrases (latest_version, recent_alerts), and compound prefixes (pipeworx_*, polymarket_*). The server is named 'Nuget' but the vast majority of tools carry pipeworx_ or polymarket_ prefixes, making the namespace feel like a grab-bag.

Tool Count2/5

35 tools is far too many for a server ostensibly named 'Nuget' — only ~5 tools relate to NuGet package lookup (search, get_package, list_versions, latest_version, scan_dependency), and even scan_dependency is npm-only. The remaining ~30 tools belong to an unrelated Pipeworx research/markets/memory platform. The count is inflated by redundant variants (ask_pipeworx trio, six polymarket tools) rather than distinct functionality.

Completeness2/5

Judged by the server's stated purpose (NuGet), the surface is thin and has dead ends: search and version metadata are covered, but there's no package owner/publisher info, no readme/description body fetch, no download stats beyond totals, and scan_dependency targets the wrong ecosystem (npm). Judged by the actual dominant domain (Pipeworx), coverage is excessive and sprawling. The tool set fails to deliver a coherent, complete surface for either apparent purpose.