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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 5,743 tools across 1500 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.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint/openWorldHint/idempotentHint. The description adds behavioral details: routing across 5,743 tools, filling arguments, returning structured answers with citation URIs, works on every tier, one fast call. No contradiction; the description complements annotations without duplicating them.

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 longer than typical but front-loads like an instruction: 'PREFER OVER WEB SEARCH' first, then scope, examples, and escalation paths. Nearly every sentence earns its place; slight redundancy around being the default entry point. Not a tight two-liner, but well-organized.

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?

Given the tool's complexity and no output schema, the description covers what it does, how it returns results (structured answer + citation URIs), when to use it, and when to use alternatives. Examples cover the domain range. Nothing an agent needs to choose this tool correctly is missing.

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

Parameters3/5

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

Schema description coverage is 100%, with the question parameter and all aliases fully documented. The description's examples ('current US unemployment rate', 'Apple's latest 10-K') illustrate acceptable inputs but don't add meaning beyond the schema. Baseline 3 is appropriate.

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?

States a specific verb and resource: 'Routes the question to the right one of 5,743 tools across 1500 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs.' Distinguishes itself from web search and names sibling tools it is not (ask_pipeworx_grounded, deep_research).

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?

Explicitly says when to use: 'PREFER OVER WEB SEARCH', 'Use whenever the user asks...', and 'START HERE for most questions — this is the default entry point.' Also gives when-not-to-use and alternatives: step up to ask_pipeworx_grounded for verbatim evidence + confidence, deep_research for broad/multi-part questions.

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

A3.9/5.0
Disambiguation3/5

Most clusters have clear roles, and the detailed routing guidance separates ask_pipeworx from deep_research and grounded mode. However, three ask_pipeworx variants (one currently identical to stable), plus overlapping opportunity-discovery tools (polymarket_edges vs bet_research) and discovery/onboarding tools (discover_tools vs suggest_questions), leave several boundary cases where an agent could select the wrong tool.

Naming Consistency3/5

Many tools group under readable prefixes (ask_pipeworx, cambridge_, polymarket_, pipeworx_) and are mostly snake_case. But the set mixes bare verbs (remember, recall, forget), verb_noun actions (resolve_entity, validate_claim), and noun-phrase names (entity_profile, recent_changes, polymarket_fill_risk), so no single convention predicts the full API.

Tool Count2/5

34 tools is well into the 'too many' band, and the count is inflated by a kitchen-sink mix of data querying, prediction-market analysis, memory, subscriptions, feedback, llms.txt generation, and npm scanning. The server name 'Data Cambridge' suggests a narrow local-data scope, which makes the sprawl look even less appropriate.

Completeness3/5

Individual verticals are fairly complete: query/grounded/beta router levels, entity resolution/profile/compare/change, prediction-market discovery through fill-risk, and full memory and subscription CRUD. The major gap is discover_tools, which returns tools promoted as ready to call directly but no generic invocation tool is exposed, so the agent must route back through ask_pipeworx.