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Glama

Macvendors

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

A4.5/5.0
Behavior4/5

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

Annotations provide only basic hints (readOnly=false, destructive=false), but the description goes further, disclosing that filing without an account returns a claim_token for later status checks, that the team reads digests daily, and that the tool is rate-limited to 5 per identifier per day. These behaviors are not inferable from annotations, so the description adds valuable context.

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 contributes: purpose, usage triggers, exclusion criteria, token mechanics, and rate limits. It's logically ordered and front-loaded with the primary purpose. While slightly verbose, it remains efficient and readable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description addresses key aspects the agent needs to know: what to report, when to use it, how the claim_token flows, and operational constraints (rate limit, free usage, daily digestion). It doesn't describe the response format, but the claim_token mention partially covers that. Given the tool is a simple feedback channel, the description is sufficiently 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?

The input schema already includes descriptions for all 4 parameters (100% coverage), but the tool description adds crucial usage semantics: it explains how to use claim_token to read previous reports, instructs users to describe issues in terms of Pipeworx tools/packs rather than pasting user prompts, and clarifies the 'type' values with real-world examples. This goes beyond the schema's generic descriptions.

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 phrase 'Tell the Pipeworx team something is broken, missing, or needs to exist,' clearly identifying the tool's purpose as reporting feedback about Pipeworx tools. It further differentiates from sibling tools like ask_pipeworx or discover_tools by explaining it's exclusively for issues with this connection's own tools, not for data queries.

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?

The description provides explicit when-to-use conditions: '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).' It also gives a clear exclusion: 'ONLY for tools served by this Pipeworx connection' and advises filing with other servers for different vendors. This is exemplary 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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, with the beta variant currently identical to stable. The five polymarket_* tools plus bet_research also blur boundaries, and discover_tools/suggest_questions both serve discovery. Detailed descriptions help, but an agent could easily select the wrong tool.

Naming Consistency3/5

All names use lower_snake_case and many follow a clear verb_noun pattern (resolve_entity, validate_claim, generate_llms_txt). However, there are notable deviations: entity_profile, pipeworx_feedback, polymarket_edges, recent_changes, and bet_research lead with nouns or adjectives. The style is readable and predictable in clusters, but not uniform.

Tool Count2/5

32 tools is well above the 25+ threshold for 'too many', and the server name Macvendors suggests a narrow MAC-lookup service, yet most tools belong to a much broader Pipeworx data/prediction-market platform. Several tools are near-duplicates or micro-variants (three ask_pipeworx versions, six polymarket tools). The set would be more appropriately split or heavily consolidated.

Completeness4/5

Within the actual broad research platform domain, the surface is fairly complete: discovery, routing, grounded answers, entity profiles, comparisons, recent changes, claim validation, semantic search, prediction-market analysis, subscriptions, memory, and feedback are all covered. Minor gaps exist, such as no subscription update tool and no batch MAC lookup, but agents can generally complete workflows without dead ends.

Resources