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Glama

Worms

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?

Annotations are all false, providing no safety/behavior hints, so the description carries the full burden. It discloses the claim_token workflow (filing without an account returns a token that can be used to read status later), rate limiting (5 per identifier per day), quota independence, and team digest review. This goes well beyond the structured fields.

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, triggers, exclusions, token mechanics, rate limits, and cost. It starts with the most important information and progressively details the workflow. No fluff or repetition.

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?

With no output schema, the description must explain return values, and it does: 'Filing without an account returns a claim_token' and how to use it. It also covers rate limits, quota, and team response, making the tool fully self-contained. Given the parameter complexity and lack of output schema, this is 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?

Schema coverage is 100%, with thorough descriptions for each parameter. The tool description adds workflow context not present in the schema, such as how claim_token is obtained and used ('Filing without an account returns a claim_token; pass it back later...') and content guidance for message ('don't paste the end-user's prompt'). This enriches parameter understanding without redundancy.

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 direct, specific statement: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It immediately distinguishes the tool from siblings by scoping it to Pipeworx-served tools and enumerating exact use cases (bug, feature/data_gap, praise). This is a model of clear purpose.

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 triggers: '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 an explicit exclusion: tools from other MCP servers. The guidance on identifying Pipeworx tools ('Pipeworx tool names are the ones this connection lists') and on not pasting end-user prompts rounds out the 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

A3.5/5.0
Disambiguation2/5

Multiple tool clusters blur together: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and bet_research all route natural-language questions to sources with overlapping response shapes. The Polymarket family is better differentiated, but an agent faces real selection risk among the query family.

Naming Consistency3/5

All tools use snake_case, which is good, but verb conventions vary widely: ask_/get_/search_/list_/validate_/generate_/scan_ plus noun-only names like ai_visibility_check, entity_profile, bet_research, and the pipeworx_* prefix. The pattern is readable but not predictable enough to guess a tool's function from its name alone.

Tool Count2/5

34 tools is well past the 25+ threshold for a coherent tool set. The server tries to be a universal data gateway, prediction-market toolkit, memory store, subscription manager, WoRMS lookup, and dependency scanner all at once—scope creep that makes the surface unreasonably large.

Completeness2/5

For a data-access server there are notable holes: pipeworx:// citation URIs are advertised as fetchable but no tool resolves them directly; subscriptions can be created, listed, and cancelled but not updated or paused; and the WoRMS component (which matches the 'Worms' server name) has only three lookup tools with no synonym/distribution/export coverage.

Resources