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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?

Beyond the read-only and idempotent annotations, the description discloses that candidate routing improvements may be active live and that none is currently active, making it behaviorally identical to ask_pipeworx for now. It also clarifies that the tool is a full working router, not a stub, and that it 'falls back to nothing.' This is exactly the kind of non-obvious behavioral context an agent needs.

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 more detailed than typical but each sentence earns its place: it establishes the beta nature, current status with a specific retirement date, usage direction, and clarification that it is a full router. It is front-loaded with the core identity, though the date-specific detail might become stale.

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 no output schema, the description covers the key context: identical arguments, identical response shape, experimental behavior, and current parity with the stable router. The only minor gap is that it does not describe what a typical response looks like, but it points to ask_pipeworx for the response shape.

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?

The input schema already documents all parameters with 100% coverage, including the aliases for question. The description only says 'same arguments' as ask_pipeworx, which reinforces the schema rather than adding new semantic meaning. Baseline 3 is appropriate since the schema carries the parameter documentation burden.

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 universal router that mirrors ask_pipeworx with the same 5,743 tools, arguments, and response shape. It explicitly positions it as the experimental edge of the stable ask_pipeworx router, so an agent can tell what it does and how it differs from siblings.

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?

It states exactly when to use the tool: 'Use it exactly like ask_pipeworx when you want the newest routing.' It also explains that results are compared against the stable router to decide what merges, giving an agent a clear context for when the experimental variant is preferable.

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

A4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose with detailed descriptions that differentiate overlapping capabilities (e.g., ask_pipeworx vs deep_research vs ask_pipeworx_grounded vs bet_research). No two tools appear redundant; even similar prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have specific scopes.

Naming Consistency5/5

Tool names consistently use lowercase snake_case (e.g., ai_visibility_check, compare_entities, pipeworx_trending, polymarket_kalshi_spread). Single-word exceptions (ephemeris, lookup, observers, recall, remember, vectors) are common short verbs and do not break the pattern. No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is above the typical 3-15 range, but the server is a comprehensive data platform covering multiple domains (SEC, FDA, FRED, prediction markets, memory, subscriptions, feedback). The count is justified given the breadth; it feels slightly heavy but not bloated or redundant.

Completeness5/5

The tool surface covers the full lifecycle for a data/research platform: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookups (ask_pipeworx, entity_profile), comparison (compare_entities), validation (validate_claim), prediction-market operations (polymarket_*), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), and meta/feedback (pipeworx_feedback, pipeworx_trending). No obvious gaps for typical workflows.