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Ask Pipeworx Beta

ask_pipeworx_beta
Read-onlyIdempotent

Beta version of ask_pipeworx: identical universal router (same 5,743 tools, same arguments, same response shape) with candidate routing improvements enabled live whenever one is under test. No candidate is active right now (the last was retired on outcome evidence 2026-07-26), so this currently matches ask_pipeworx exactly. Use it exactly like ask_pipeworx when you want the newest routing; results are compared against the stable router to decide what merges. Falls back to nothing — this IS a full working router, just the experimental edge.

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

A4.5/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond annotations: it is an experimental edge, candidate routing may be live, results are compared against the stable router, and it is a full working router rather than a fallback stub.

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 and information-dense, with each sentence adding useful context. It is slightly wordy in places, such as restating that it currently matches ask_pipeworx, but overall it remains well-structured.

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?

With no output schema, the description compensates by stating that the response shape matches ask_pipeworx and that this is a fully functional router. It relies on the agent knowing ask_pipeworx for exact return details, which prevents a perfect score.

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 every parameter already documented as an alias for 'question'. The description adds no parameter-level detail beyond noting identical arguments to ask_pipeworx, so the baseline score of 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?

The description clearly identifies the tool as a beta version of ask_pipeworx, a universal router with the same 5,743 tools, arguments, and response shape. It distinguishes the tool from its stable sibling by emphasizing the experimental routing-improvement context.

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 states 'Use it exactly like ask_pipeworx when you want the newest routing' and names the stable alternative. It also clarifies that no candidate is currently active, so usage is currently identical to ask_pipeworx.

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
Disambiguation2/5

Many tools serve overlapping purposes (multiple ask_pipeworx variants, several entity tools, multiple Polymarket edge tools), and while descriptions are detailed, an agent would struggle to quickly select the correct tool without careful reading.

Naming Consistency2/5

Naming conventions are mixed: some use verb_noun (ask_pipeworx, extract_text), others use noun_phrase (ai_visibility_check, bet_research, entity_profile), and no clear pattern dominates.

Tool Count3/5

32 tools is above the typical well-scoped range (3-15). While the broad domain of data query, prediction markets, memory, and subscriptions somewhat justifies the count, it still feels heavy and could benefit from consolidation.

Completeness4/5

The tool set covers most operations for its domain: data query (with multiple depth levels), memory CRUD, subscription lifecycle, and utilities like OCR and dependency scanning. Minor gaps exist (e.g., no direct modify operation), but overall it's fairly complete.