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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.

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (which only say readOnlyHint=false), the description discloses key behaviors: rate-limiting to 5 per identifier per day, the claim_token mechanism for later status checks, that it is free and doesn't count against quota, and that the team reads digests daily. This significantly enriches the agent's understanding of side effects and constraints.

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 dense yet every sentence adds unique information—scope, alternatives, return mechanism, rate limit, and contribution to roadmap. It is front-loaded with the core purpose and uses clear structuring, making it easy to parse 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?

Given the lack of an output schema, the description adequately covers return behavior (claim_token and reading later), prerequisites (no account required), constraints (rate limit), and scope boundaries. It is complete for a feedback tool with optional parameters and no required fields.

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?

While the schema already covers 100% of parameters, the description adds meaningful usage context: it instructs users not to paste the end-user's prompt, explains how to use claim_token (pass it back with no other arguments to read the reply), and frames the context parameter as optional structured context. This goes beyond the schema's baseline 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 begins with a specific verb and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes this from sibling tools by stating it is only for Pipeworx-served tools and not for tools from other MCP servers, making its purpose unambiguous.

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 states when to use: for bugs, feature/data_gap requests, and praise. Provides a clear exclusion: do not use for tools from other MCP servers, and directs users to file with that server instead. Also clarifies what counts as a Pipeworx tool, giving concrete when/when-not 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

A3.7/5.0
Disambiguation2/5

The set mixes Salesforce CRUD tools with a large Pipeworx research and prediction-market platform, and several tools overlap heavily: ask_pipeworx vs ask_pipeworx_beta are functionally identical, grounded/validate_claim/deep_research cover similar lookup/verification territory, and the six polymarket/bet tools share edge-finding purposes with only subtle distinctions. An agent would need to read long descriptions carefully to pick the right one, so misselection risk is high.

Naming Consistency3/5

Salesforce tools follow a clear sf_verb_noun pattern, and the Pipeworx tools mostly use lowercase snake_case phrase names, but the conventions diverge: ask_pipeworx has no underscore, deep_research/entity_profile are noun phrases rather than verb-first, and the sf_* prefix is a separate naming family. It is still readable, but it is not a single predictable pattern.

Tool Count2/5

39 tools is well past the heavy threshold, and most belong to a broad data/research platform rather than the Salesforce scope implied by the server name; only 8 tools are actually Salesforce CRUD/query operations. The count feels bloated for a coherent assistant, even if individual features are useful.

Completeness4/5

The Salesforce subset is complete: create/get/update/delete/query/search/describe/list-object cover the record lifecycle with no dead ends. The broader Pipeworx ecosystem also has strong coverage, including routing, grounded verification, research, entity resolution, memory, and subscriptions, with only minor gaps such as no direct citation-fetch tool and no Salesforce upsert/bulk operations.

Resources