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Ask Pipeworx Beta

ask_pipeworx_beta
Read-onlyIdempotent

Beta version of ask_pipeworx: identical universal router (same 5,798 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.

Schema Changelog

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

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already signal read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: candidate improvements are enabled live, the last candidate was retired on a specific date, results are compared against the stable router, and it 'falls back to nothing' meaning it is a fully functional router, not a stub. This transparency goes beyond the annotations.

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 compact and front-loaded, leading with the beta identity and router scope. Each sentence earns its place by covering current state, usage, and a clarification that this is a full working router. It is slightly dense but not verbose or redundant.

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 whose output shape matches ask_pipeworx, the description covers the essential context: what it is, how it differs, when to use it, and its current runtime state. It does not describe return values, but the 'same response shape' clause covers that by reference. It is sufficiently complete for an agent to decide when to invoke it.

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 description does not discuss parameters, but the input schema has 100% description coverage. The schema thoroughly documents the question parameter and its aliases, so the description does not need to add parameter details. A baseline score of 3 is appropriate given the schema handles the 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 version of ask_pipeworx, an identical universal router with the same 5,798 tools, arguments, and response shape. It distinguishes itself from the stable ask_pipeworx by enabling candidate routing improvements under test, making the purpose and scope immediately understandable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool: 'Use it exactly like ask_pipeworx when you want the newest routing.' It also notes that no candidate is currently active, so it matches ask_pipeworx exactly right now. It does not explicitly mention the grounded alternative or state when not to use it, but the 'experimental edge' label implies caution for production use.

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
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., ask_pipeworx vs. deep_research vs. validate_claim). However, a few pairs like ask_pipeworx_beta vs. ask_pipeworx and validate_claim vs. ask_pipeworx_grounded have overlapping roles, even though descriptions do differentiate them.

Naming Consistency4/5

All tool names use snake_case consistently, and many follow a verb_noun pattern (ask_pipeworx, compare_entities, subscribe). Some exceptions like entity_profile, recent_alerts, and pipeworx_trending break the strict verb_noun pattern but remain readable and stylistically uniform.

Tool Count3/5

With 35 tools, this is above the typical 'well-scoped' range and exceeds the 25-tool threshold for heavy servers. However, the server covers a very broad domain (financial data, prediction markets, agriculture, AI visibility, memory, subscriptions), which partially justifies the count, but it still feels bloated.

Completeness4/5

The tool surface covers all major workflows: data querying (ask_pipeworx, deep_research), entity resolution and comparison, prediction-market analysis, agricultural data (FAS tools), memory (remember/recall/forget), and subscription management (subscribe/unsubscribe/list). The only minor gap is lack of direct write/update operations for external data, but that's not expected for a read-heavy platform.