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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

A4.7/5.0
Behavior5/5

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

Beyond the annotations (which only indicate readOnlyHint=false, etc.), the description reveals rate limiting ('Rate-limited to 5 per identifier per day'), the claim_token workflow for checking status, and that the team reads digests daily. These are behavioral traits not present in annotations, adding real transparency.

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 long (about 150 words) but front-loaded with the core action, and every sentence serves a purpose: what, when, exclusions, claim_token mechanics, rate limits. It could be tightened, but the organization is logical and no filler exists.

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?

With 4 parameters, no output schema, and nested objects, the description covers the essential usage scenarios, including the claim_token follow-up flow, which is a non-obvious behavior. It doesn't spell out the exact response structure, but since there is no output schema and the main response is implied (claim_token or status), 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?

Schema coverage is 100%, so the baseline is 3. The description goes beyond the schema by explaining the claim_token lifecycle ('Filing without an account returns a `claim_token`; pass it back later... to read whether it was fixed'), which adds semantic depth not captured in the parameter description. It also clarifies that claim_token should be used alone. A slight deduction for not elaborating on the 'type' parameter beyond the schema, but the schema already covers it.

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 and resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes this from sibling tools by framing it as the sole feedback channel, and even enumerates feedback categories (bug, feature, data_gap, praise).

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?

Provides explicit when-to-use guidance: '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).' Also gives a clear exclusion: 'if the tool came from a different MCP server... file it with that server instead.' This is textbook usage 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

B3.1/5.0
Disambiguation1/5

The tool set includes multiple pairs of nearly identical tools (e.g., ask_pipeworx and ask_pipeworx_grounded, bet_research and polymarket_arbitrage) that overlap heavily in purpose. Many tools also combine unrelated functions, making it difficult for an agent to select the right one without confusion.

Naming Consistency1/5

Tool names follow no discernible pattern: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, get_bill), and even lengthy descriptive names (scan_competitor_ai_presence) are mixed. The naming style is chaotic and inconsistent across the set.

Tool Count2/5

With 30 tools, the count is excessive for a server supposedly focused on OpenStates (state legislatures). Only a few tools (search_bills, get_bill, etc.) relate to the server's name, while the rest are tangentially related to data lookups, betting, or AI visibility, making the set bloated.

Completeness1/5

The server's core domain (state legislative data) is severely underserved: only about 5 tools cover bills and legislators, lacking basic CRUD operations like create, update, or delete. Many obvious operations (e.g., searching bills by subject, tracking votes) are missing, while unrelated tools dominate.