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Ipqualityscore

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

A5/5.0
Behavior5/5

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

Annotations are all false and offer no safety cues, so the description carries the full burden. It discloses rate limiting (5 per day), the claim_token follow-up mechanism, daily digest reads affecting roadmap, and a privacy/format constraint (don't paste end-user prompt). This is rich behavioral context well beyond what annotations provide.

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?

Although lengthy, every sentence adds new information: purpose, criteria, exclusions, content rules, follow-up mechanics, rate limits, and pricing. The structure is front-loaded with the core purpose and organized 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?

The tool has a nested context object and no output schema, yet the description covers the entire workflow: when to file, what to include, what to expect (claim_token), how to check status, and constraints (rate limit, free). The sibling list confirms this is a standalone feedback channel, so all necessary context is present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already covers 100% of parameters, but the description adds meaning: it explains the claim_token parameter's special usage (pass with no other arguments to read status) and provides message guidance ('1-2 sentences typical') beyond the schema's character limit. This enhances actionable understanding.

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 'Tell the Pipeworx team something is broken, missing, or needs to exist,' using a clear verb and resource (Pipeworx feedback). It clearly distinguishes itself from sibling tools by focusing exclusively on reporting feedback about Pipeworx tools, not on investigation or data retrieval.

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?

It explicitly enumerates when to use: bug, feature/data_gap, praise. It provides a strong exclusion: if the tool came from a different MCP server, file it there instead, and gives a decision rule ('Pipeworx tool names are the ones this connection lists'). This leaves no ambiguity about applicability.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Ipqualityscore toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Multiple natural-language query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, suggest_questions) have heavily overlapping purposes, and the descriptions rely on subtle caveats to differentiate them. Similarly, entity_profile vs compare_entities vs recent_changes and ai_visibility_check vs scan_competitor_ai_presence blur boundaries. Only the four check_* tools (email/ip/phone/url) are cleanly distinct.

Naming Consistency2/5

There are some consistent prefixes (check_*, polymarket_*, ask_pipeworx_*, pipeworx_*) but the overall set mixes verb_noun, noun_verb, and standalone adjectival names (deep_research, entity_profile, bet_research, validate_claim, recent_changes). The pattern is readable within families but chaotic across the whole surface, with no unified convention.

Tool Count2/5

35 tools is excessive for a server branded 'Ipqualityscore', especially since only 4 tools actually serve that fraud-checking domain. The rest sprawls into general data research, prediction-market analysis, memory management, subscriptions, and npm dependency scanning — a far larger scope than the name implies. This is a scattershot collection rather than a coherent offering.

Completeness2/5

The IPQS core domain is thin (only email, IP, phone, URL checks) and missing common fraud-screening operations like transaction scoring or domain reputation. Conversely, the Pipeworx side is over-complete with redundant query paths, while unrelated subsystems (memory, subscriptions, feedback) create dead ends that don't serve the server's apparent purpose. The lack of a clear domain makes genuine completeness impossible to assess or claim.

Resources