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Glama

Pride

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

A4.9/5.0
Behavior5/5

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

The description goes far beyond the sparse annotations (all hints false) by disclosing rate limits (5/day), quota-free nature, the claim_token flow for later retrieval, and the fact that the team reads digests daily. It also explains side effects of filing without an account. 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 serves a purpose: purpose, usage, exclusions, claim-token mechanics, rate limits, and roadmap impact. It is front-loaded with the core action and logically organized. No filler or redundant repetition of schema fields.

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 four params, the description explains the return behavior (claim_token on filing, status/resolution when querying with the token), which is essential. It also covers constraints like rate limits and scope. Given the tool's complexity, the description is fully sufficient for correct invocation.

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%, earning a baseline of 3, but the description adds meaningful guidance beyond the schema: 'Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt' for the message field, and it explains the claim_token usage pattern (pass `pwfb_…` with no other arguments). These are extra semantic constraints not present 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?

The description opens with a clear, specific verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It immediately establishes the tool's scope (feedback about Pipeworx tools) and distinguishes it from all sibling tools, which are research/query utilities. No ambiguity exists.

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?

Explicitly lists when to use: for bugs, feature requests, data gaps, or praise. It also provides a clear exclusion: 'ONLY for tools served by this Pipeworx connection' and tells users to file with a different MCP server otherwise. This is textbook alternative/context 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 are near-identical in purpose: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same style of question, and the company-research tools (entity_profile, compare_entities, recent_changes) and Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) have overlapping triggers. The long descriptions help, but an agent could easily select the wrong one.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb-led (ask_pipeworx, resolve_entity, scan_dependency), some are noun-led (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective-noun phrases (recent_changes, recent_alerts). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set has no unifying pattern.

Tool Count2/5

33 tools is high and the server bundles several unrelated domains: Pipeworx data research, prediction markets, PRIDE proteomics, memory, subscriptions, and AI-visibility checks. Each cluster is individually useful, but the aggregate surface feels over-stuffed rather than well-scoped for a single MCP server.

Completeness4/5

The main clusters are well covered: query/grounded/deep research, entity resolution/profile/compare/validate, memory CRUD, subscription lifecycle, and a rich Polymarket analytics toolkit. Minor gaps exist—PRIDE is limited to metadata search/get and there is no trade execution for prediction markets—but most workflows can be completed without dead ends.

Resources