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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

A4.6/5.0
Behavior5/5

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

The description adds meaningful behavioral context beyond the annotations: it is an experimental beta with candidate routing improvements enabled live, it falls back to nothing rather than delegating, and it is a full working router despite being an experimental edge. It also gives a concrete status update (last candidate retired on 2026-07-26), which helps the agent interpret current behavior. This is consistent with the readOnly and idempotent annotations since it only routes queries.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the most important fact: it is a beta version of ask_pipeworx with identical behavior. Each sentence earns its place: identity of the router, current candidate status, and explicit usage guidance. The parenthetical retirement date is a concrete detail that adds transparency without bloating the description.

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?

Given the tool's complexity as a universal router, the description covers its relationship to ask_pipeworx, its current experimental status, and its expected behavior clearly. The absence of an output schema is mitigated by stating the response shape is identical to ask_pipeworx. It does not describe what an actual response looks like, but the sibling reference is sufficient for agents that already know ask_pipeworx.

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 description coverage is 100%, so the baseline is 3, but the description adds value by stating that the arguments are identical to ask_pipeworx's 5,798-tool routing interface and that the response shape is the same. This tells an agent familiar with ask_pipeworx to reuse its parameter knowledge without inspecting the schema further. It does not enumerate each parameter, but that is largely unnecessary given the schema already covers them.

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 specific purpose: this is the beta version of ask_pipeworx, a universal router with the same 5,798 tools, arguments, and response shape. It clearly distinguishes itself from the sibling tools by labeling it as the experimental edge router and explaining that it may have candidate routing improvements enabled. An agent can understand exactly what this tool does and how it differs from ask_pipeworx.

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 tells the agent to use it exactly like ask_pipeworx when the newest routing behavior is desired, and it names ask_pipeworx as the stable reference point. It also notes the current state ('No candidate is active right now') so the agent knows it currently matches ask_pipeworx exactly. It does not explicitly state when not to use it, such as preferring the stable router for production-critical decisions, so it falls just short of a 5.

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 for general queries, ask_pipeworx_grounded for high-stakes verification, deep_research for multi-faceted research). However, some overlap exists among the ask_* variants and the prediction-market tools (bet_research vs. polymarket_edges vs. polymarket_arbitrage), which could cause misselection without careful reading of the detailed descriptions.

Naming Consistency4/5

Tool names consistently use snake_case and mostly follow the verb_noun pattern (e.g., list_subscriptions, resolve_entity, validate_claim). Minor deviations like random_fact and today_fact (adjective_noun) and pipeworx_feedback (noun_noun) introduce slight inconsistency, but the overall pattern is predictable.

Tool Count3/5

With 33 tools, the count exceeds the typical 3-15 range and even the 16-25 'heavy' threshold. While the server covers an unusually broad domain (data retrieval, prediction markets, memory, subscriptions, AI visibility), several tools could be consolidated (e.g., the six polymarket tools, trivial random_fact/today_fact). The scope partially justifies the count, but it feels over-provisioned.

Completeness4/5

The tool surface is remarkably comprehensive for a data platform: it covers querying, entity resolution, comparison, change feeds, memory persistence, subscription management, validation, and even meta-tool discovery. Minor gaps exist (e.g., no explicit update/delete for external data, but that is not the service's purpose). Overall, no obvious dead ends.