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words

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

A4.5/5.0
Behavior4/5

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

Discloses rate limiting (5 per identifier per day), free usage, and the claim_token mechanism for later reads. Annotations are all false and the description adds significant behavioral context beyond them, without contradicting anything.

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?

Longer than typical but every sentence carries operational weight. It is front-loaded with the main purpose and usage triggers, and the rest covers important exclusions, limits, and token behavior. No filler, though slightly verbose.

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?

Covers core usage, exclusions, rate limits, and the follow-up token mechanism. Since there is no output schema, it sufficiently explains the return token behavior. The distinction between account vs. no-account filing is only implied, leaving minor ambiguity.

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 baseline is 3. The description adds value by explaining how to use claim_token (passing it back to read status) and advising not to paste end-user prompts, enriching the meaning of the message and claim_token parameters.

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 clearly states the tool's purpose: telling the Pipeworx team about something broken, missing, or needing to exist. It distinguishes itself from siblings like ask_pipeworx by focusing on feedback reporting rather than questions, and explicitly scopes to Pipeworx-served tools.

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: bug, feature/data_gap, praise. Also gives exclusions (tools from other MCP servers) and directs users to file elsewhere. Explains the claim_token flow for checking resolution status.

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

A3.6/5.0
Disambiguation3/5

Most tools have distinct purposes, but there is overlap between query tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research, and between prediction market tools like bet_research and polymarket_edges. Descriptions help differentiate, but the boundaries are not always clear.

Naming Consistency2/5

Tool names are all snake_case but lack a consistent pattern. Some start with verbs (ask, compare, find), others are nouns (autocomplete, entity_profile), and many are long phrases (ask_pipeworx_grounded, scan_competitor_ai_presence). The naming feels ad-hoc and not easy to predict.

Tool Count2/5

At 35 tools, this server is over-packed for a server named 'words'. Many tools are unrelated to words (e.g., prediction markets, subscriptions, entity profiles). The scope is too broad, making it feel like a catch-all rather than a coherent set.

Completeness3/5

The word tools are limited (only 6), leaving obvious gaps for a word-focused server (e.g., no dictionary lookup, no word definitions). However, the server covers a wide range of data domains through meta-tools like ask_pipeworx, which compensates but makes the purpose unclear.

Resources