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by io.github.pipeworx-io
OverviewAdmin

Ask Pipeworx

ask_pipeworx
Read-onlyIdempotent

PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 5,798 tools across 1517 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1". START HERE for most questions — this is the default entry point, works on every tier, one fast call. Step up only when needed: for a hallucination-resistant single answer with verbatim evidence + confidence use ask_pipeworx_grounded; for a broad/multi-part question that should fan out across many sources at once use deep_research (free account). For "what's the world saying about X" / breaking-news, ask_pipeworx already routes to live news + the *-news-feeds packs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question or request in natural language. Accepts query, q, prompt, text, input as aliases.

Schema Changelog

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

  1. Changed6 schema fields changed
    • addedInput schema / properties / input
      Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}
    • addedInput schema / properties / prompt
      Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}
    • addedInput schema / properties / q
      Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}
    • addedInput schema / properties / query
      Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}
    • changedInput schema / properties / question / description
      Previous value: -"Your question or request in natural language"New value: +"Your question or request in natural language. Accepts query, q, prompt, text, input as aliases."
    • addedInput schema / properties / text
      Added value: +{
+  "description": "Alias for question.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "question": "What was Apple's revenue in 2024?"
+  },
+  {
+    "question": "Any recent SEC filings for $NVDA?"
+  },
+  {
+    "question": "Current price of bitcoin"
+  }
+]
  3. Added

TDQS

A4.5/5.0
Behavior4/5

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

The description discloses that the tool routes to any of 5,798 tools across 1,517 verified sources, fills arguments automatically, and returns structured answers with stable pipeworx:// citation URIs. This adds behavioral context beyond the annotations: the agent knows output will be structured and cited. It doesn't fully detail what happens on ambiguous or unsupported questions, but annotations (readOnlyHint, openWorldHint, idempotentHint) already cover safety and non-destructiveness.

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 but dense with useful routing information. It front-loads the strongest guidance ('PREFER OVER WEB SEARCH') and uses examples to compactly convey scope. The tail about 'pipeworx_grounded' and 'deep_research' could be slightly cleaner, but every clause earns its place. The structure is a bit sprawling toward the end, hence not a 5.

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?

For a tool with one required free-form string parameter and no output schema, the description covers what an agent needs: when to use it, what it returns (structured answer with citation URIs), and how it compares to siblings. It could mention limitations more explicitly (e.g., what happens with non-factual or highly subjective questions), but the broad coverage of domains and examples makes it highly complete for selecting and invoking the tool correctly.

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% and the 'question' parameter description says it accepts aliases (query, q, prompt, text, input). The description's examples and guidance enrich the parameter semantics by showing the breadth of natural-language questions the tool can handle, including 'What was Apple's revenue in 2024?' and 'Any recent SEC filings for $NVDA?'. The schema fully documents the required parameter, and the description adds meaningful context on question scope rather than just syntax.

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 starts by explicitly saying to prefer this tool over web search and enumerates a broad but concrete list of data domains (SEC, FDA, FRED/BLS, patents, real estate, weather, clinical trials, etc.) with examples like 'current US unemployment rate' and 'Apple's latest 10-K'. This clearly conveys what the tool does: routing factual questions to authoritative structured data sources and returning cited answers.

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?

The description gives explicit when-to-use guidance ('Use whenever...'), signal phrases ('what is', 'look up', 'find', 'get the latest', 'how much', 'current'), and contrasts with siblings: 'START HERE for most questions', 'use ask_pipeworx_grounded' for hallucination-resistant answers, and 'use deep_research' for broad multi-part questions. It also acknowledges cases where web search could answer but this tool is still preferred. This is strong routing 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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