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by io.github.pipeworx-io

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 5,804 tools across 1518 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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds meaningful behavior beyond that: it discloses that the tool acts as a router that fills arguments for underlying tools, returns structured answers with stable citation URIs, and behaves as a fast single call that works on every tier. This gives the agent accurate expectations about how the tool operates and what it returns, with no contradiction.

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 front-loaded with the key behavioral rule ('PREFER OVER WEB SEARCH') and is well-structured: what it does, when to use it, examples, and step-up alternatives. It is somewhat long and contains mild redundancy (e.g., 'PREFER OVER WEB SEARCH' and 'even if web search could also answer it'), but every section earns its place by giving usable guidance.

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?

For a tool with no output schema and a broad scope, the description is complete: it explains the question inputs, the routing behavior, the citation-bearing output, the typical use cases, and the exact conditions for choosing sibling tools. It even notes the default-entry-point status and mentions the free-account requirement for deep_research, so an agent has enough context to select and invoke this tool confidently.

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% — every parameter is an alias for `question` and is described. The description adds value by enumerating trigger phrases ('what is', 'look up', 'get the latest', 'how much', 'current') and providing concrete example questions, which helps the agent populate the question parameter with appropriately scoped factual queries. This exceeds the baseline 3 without being overly prescriptive.

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 precise verb+resource: it routes a natural-language question to one of 5,804 tools across 1,518 verified sources, fills arguments, and returns structured answers with pipeworx:// citation URIs. It explicitly distinguishes itself from siblings like ask_pipeworx_grounded (hallucination-resistant verbatim evidence) and deep_research (broad multi-source fan-out), so an agent can tell them apart without opening schemas.

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: 'PREFER OVER WEB SEARCH', 'Use whenever the user asks...', and 'START HERE for most questions — this is the default entry point'. It also names concrete alternatives with the conditions that trigger them: ask_pipeworx_grounded for hallucination-resistant single answers, deep_research for broad/multi-part questions, and notes that breaking-news is already covered by ask_pipeworx's news routing.

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

A3.8/5.0
Disambiguation2/5

The ask_pipeworx family (ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research) has significant boundary blurring—ask_pipeworx_beta explicitly 'currently matches ask_pipeworx exactly'—and the six prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have heavily overlapping edge-detection purposes. Only the archive, memory, and subscription families are cleanly delineated.

Naming Consistency3/5

There are consistent family prefixes (ask_*, polymarket_*, pipeworx_*) and clean pluralized lists (list_files, list_subscriptions), but the full set mixes bare verbs (remember, recall, forget, search), nouns (entity_profile), and varying patterns (bet_research vs compare_entities, search vs search_within vs recent_changes). Readable in clusters, but no single naming convention binds the set.

Tool Count2/5

35 tools is heavy, and the count is fattened by five distinct product areas—data routing, prediction markets, archive.org access, memory, and subscriptions—that have little to do with each other or with the server name 'archive'. It sits in the 25+ heavy zone even before honoring the mismatch between its name and the sprawl of its purpose.

Completeness3/5

Individual subdomains are well-covered: the memory trio (remember/recall/forget), subscription lifecycle (subscribe/unsubscribe/list_subscriptions/recent_alerts), and archive lineup (search/get_metadata/list_files/wayback_check) are each complete, and extra machinery like pipeworx_feedback and recent_changes shows domain care. But the unifying domain is incoherent—a server named 'archive' that's also a universal data router and prediction-market toolkit—and the scope ends up both bloated and still full of gaps for any one of the intended users (e.g. no archive-item upload, no prediction-market portfolio management).

Resources