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Walkscore

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?

Annotations are all false (readOnly, openWorld, idempotent, destructive) and offer no insight. The description compensates fully by revealing rate limits (5/day/identifier), free usage (no quota), the claim_token follow-up mechanism, and how the team processes feedback. It even explains behavior when no account is provided. 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 dense but every sentence serves a purpose: purpose, when-to-use, when-not-to-use, message guidance, token mechanics, rate limits, and cost. It is front-loaded with the main verb and resource, then expands logically. No filler or redundancy.

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?

Despite no output schema, the description explains what to expect (a claim_token when filing without an account, and status updates later). It covers all necessary operational details: rate limiting, cost, and follow-up workflow. For a feedback tool of this complexity, nothing important is missing.

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 description coverage is 100%, so baseline is 3. The description adds extra semantics beyond the schema: how to use claim_token ('pass it back later as pipeworx_feedback({claim_token:"pwfb_…"})'), message formatting guidance ('Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt'), and typical message length. This is meaningful added value, earning a 4.

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 states a specific verb+resource: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes from sibling tools like ask_pipeworx or discover_tools by focusing on feedback, not Q&A or discovery.

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?

Usage is explicitly codified: '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 for tools from other MCP servers and tells users to file elsewhere. This is a textbook when-to-use/alternatives explanation.

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

A4.1/5.0
Disambiguation3/5

Tools are generally distinct but several clusters (ask_pipeworx family, polymarket family) have highly similar names that could cause confusion. For example, ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all appear to do similar things with subtle differences. The agent would need to read descriptions carefully to pick the right one.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., compare_entities, resolve_entity, validate_claim). However, a few are single verbs (remember, recall, forget) or have inconsistent suffixes (ask_pipeworx_beta, walkscore_score). This is mostly consistent but not perfectly uniform.

Tool Count3/5

32 tools is quite high, bordering on excessive. While each tool serves a distinct purpose, the sheer number may overwhelm the agent. However, the tools cover a wide range of functionality, so the count is not unreasonable for a comprehensive data platform.

Completeness4/5

The tool set is comprehensive for querying data, conducting research, managing subscriptions, and evaluating bets. It includes meta-tools for discovery and memory. Notable gaps include user account management and direct file handling, but overall coverage is strong.

Resources