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Glama

Librivox

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

A4.8/5.0
Behavior5/5

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

The description discloses important behavioral traits not visible in annotations (which are all false and thus uninformative): the claim_token flow (filing returns a token, passing it later reads status), rate limiting ('5 per identifier per day'), that it's free and doesn't count against quota, and that the team reads digests daily and signals affect roadmap. It also advises against pasting the end-user's prompt, which is a nuance that helps the agent act appropriately.

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 longer than average but every sentence adds value: purpose, usage triggers, scoping restrictions, content guidance, token mechanism, rate limits, and quota impact. It is front-loaded with the core purpose and structured logically. It could be tightened slightly, but it avoids redundancy and fluff, making it effective despite its length.

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?

Despite having no output schema, the description fully covers the tool's behavior: what happens on filing (returns claim_token), how to retrieve resolution status, rate limits, quota impact, and content requirements. It also addresses edge cases (feedback about non-Pipeworx tools, uncertainty about which tools are Pipeworx). For a feedback tool with nested context objects and multiple optional params, this is exceptionally complete.

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?

Input schema already covers all 4 parameters with detailed descriptions (100% coverage), so baseline is 3. The description adds meaningful context beyond the schema, specifically explaining the claim_token usage pattern ('Filing without an account returns a claim_token; pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})') and emphasizing that feedback should be described in terms of Pipeworx tools/packs. This elevates it above baseline but doesn't enumerate all parameters, so not a 5.

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 clear verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It then enumerates specific categories (bug, feature/data_gap, praise) which clearly distinguishes it from sibling research tools like ask_pipeworx or deep_research. It also explicitly scopes the tool to feedback about Pipeworx tools only, differentiating it from any other MCP server feedback paths.

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?

Explicit 'Use when' conditions are provided for each feedback type: bug (wrong/stale data), feature/data_gap (missing tool), and praise. It also gives a clear exclusion: 'if the tool came from a different MCP server... file it with that server instead,' plus a disambiguation tip ('Not sure? Pipeworx tool names are the ones this connection lists'). This is far beyond a basic usage hint.

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

The set mixes several unrelated domains (LibriVox, Pipeworx data lookup, Polymarket betting, memory, subscriptions), and within those domains there is heavy overlap: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all route to the same 5,798 tools, and polymarket_edges/polymarket_arbitrage/polymarket_edge_tracker/polymarket_fill_risk/bet_research all scan prediction-market opportunities. An agent can easily pick the wrong tool when the same question fits several of them.

Naming Consistency3/5

Names are mostly lowercase snake_case, but the patterns are inconsistent across domains: some are verb-first (ask_pipeworx, compare_entities, generate_llms_txt), some are noun-first (audiobook, tracks, polymarket_edges), and pluralization varies (audiobook vs audiobooks, authors vs tracks). The Pipeworx family is internally consistent, but the overall set has no unified convention.

Tool Count2/5

35 tools is heavy for a server named Librivox, and only 4 of them (audiobook, audiobooks, authors, tracks) actually relate to LibriVox. The remaining ~31 tools (Pipeworx, Polymarket, memory, subscriptions, AI visibility) make the count far exceed what the server name and apparent purpose suggest.

Completeness2/5

For a LibriVox-focused server, the tool surface is thin: search and fetch audiobooks, search authors, and list tracks, but no browse by genre, no reader/search-by-reader, no language filter, no author detail endpoint. The Pipeworx side is quite comprehensive, but it does not make up for the gap relative to the server's stated identity.

Resources