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Hackertarget

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changed
    • addedInput schema / properties / claim_token
      Added value: +{
+  "description": "Read 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.",
+  "type": "string"
+}
    • removedInput schema / required
      Removed value: -[
-  "type",
-  "message"
-]
  2. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations provide no hints (all false), so the description carries the full burden. It discloses rate limiting ('5 per identifier per day'), the claim_token flow for later retrieval, that it's free and doesn't count against quota, and that the team reads digests daily. It also warns not to paste end-user prompts. This is rich, non-obvious behavioral context.

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 earns its place. It front-loads the core purpose, then efficiently covers use cases, exclusions, token mechanics, rate limits, and impact. No fluff or repetition; it's proportionate to the tool's complexity.

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 fully explains the return behavior via claim_token and how to check resolution status. It also covers what to include (Pipeworx tools/packs) and what to avoid (end-user prompts), plus rate limits and policy. The context is complete for an effective feedback tool.

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% with detailed parameter descriptions. The description adds value beyond the schema by clarifying the intended use of each feedback type, reinforcing the claim_token invocation pattern with a concrete example, and adding instructions like 'don't paste the end-user's prompt.' This goes beyond the schema's baseline, though the schema already covers most semantics.

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+resource directive: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It clearly distinguishes this tool from siblings by specifying it is ONLY for tools served by this Pipeworx connection, contrasting with other MCP servers (Gmail, Splunk, Slack). This makes the tool's purpose unmistakable and unique.

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?

Explicit when-to-use guidance is provided with concrete scenarios: '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 a clear exclusion rule: tools from other MCP servers should be reported elsewhere. This fully covers when and when not to use.

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

C2.6/5.0
Disambiguation3/5

Tools like ask_pipeworx, ask_pipeworx_grounded, deep_research, and entity_profile have overlapping purposes, causing potential confusion. However, their descriptions provide some differentiation, so an agent can usually pick the right one with careful reading.

Naming Consistency2/5

Naming is highly inconsistent: mixes verb_noun (ask_pipeworx), noun_verb (reverse_dns), single-word (geoip), and compound phrases (generate_llms_txt). No clear pattern, making it hard for an agent to predict tool names.

Tool Count2/5

44 tools is overwhelmingly high for a single server. The set mixes unrelated domains (network tools, data APIs, memory, prediction markets), suggesting it's a grab bag rather than a focused toolkit.

Completeness2/5

The server lacks a coherent domain, so evaluating completeness is difficult. There are many lookup tools but few for updates or deletes (except memory). The HackerTarget subset is sparse, and the overall surface feels incomplete for any single purpose.

Resources