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Glama

Tarot Draw

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Annotations are minimal (no readOnly/destructive hints), so the description carries the burden. It discloses the two-step flow (filing returns a claim_token, which can be passed back later to read resolution), rate limiting (5 per identifier per day), and that it doesn't count against tool-call quota. 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 dense; every sentence adds value—purpose, use cases, exclusions, usage pattern, rate limit, and expectations. It's front-loaded with the primary action and has no filler.

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 two distinct modes (reporting and checking), the description explains both, including the return value (claim_token) absent from schema, and sets expectations (daily reads, roadmap impact). It's fully adequate without an output schema.

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 already covers all four parameters with rich descriptions (enum meanings, claim_token usage), providing baseline 3. The description adds extra context: how to use claim_token as a follow-up call and guidance to describe issues in terms of Pipeworx tools/packs, enhancing parameter understanding.

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+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It distinguishes from siblings by explicitly limiting scope to tools served by this Pipeworx connection, so the agent knows exactly what the tool does.

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 trigger conditions (bug, feature/data_gap, praise) and provides an exclusion: do not report issues for tools from other MCP servers. It also says 'Not sure?' and tells agent to check which connection lists it. This is strong guidance with clear alternatives.

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

A3.7/5.0
Disambiguation2/5

Multiple tool families have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, suggest_questions and discover_tools are near-duplicates, and ai_visibility_check is a subset of scan_competitor_ai_presence. The five polymarket_* tools are heavily overlapping in purpose and rely on long descriptions to distinguish them, which an agent must read carefully to avoid misselection.

Naming Consistency3/5

All names are lowercase snake_case and there are helpful prefixes (polymarket_*, ask_pipeworx_*, pipeworx_*), but the verb/noun ordering is inconsistent: verb-first names (generate_llms_txt, resolve_entity, scan_dependency) sit alongside noun-first names (bet_research, entity_profile, recent_changes) and bare verbs (forget, recall, remember). Sub-families are internally consistent, but the set as a whole follows no single convention.

Tool Count2/5

32 tools is over the 'too many' threshold, and the scope is a grab-bag rather than a focused server: data research, prediction-market analysis, npm dependency checks, llms.txt generation, memory utilities, subscriptions, and exactly one tarot tool. The server is named 'Tarot Draw' yet 31 of 32 tools serve a completely different purpose, making the count wildly mismatched to the apparent identity.

Completeness2/5

For the inferred Pipeworx data/prediction-market domain the coverage is genuinely deep — ask/grounded/deep research, entity resolution, profiles, comparisons, validation, subscriptions, alerts, edge tracking, and arbitrage all exist. But for the stated purpose ('Tarot Draw'), the surface is one draw tool with no deck details, spreads, reading history, or reversal support, and the data tools' domain is so diffuse that an agent cannot rely on the set forming a coherent workflow.

Resources