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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

A4.3/5.0
Behavior4/5

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

Annotations already include readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, which are comprehensive for safety and side-effect clarity. The description adds that this is the experimental edge, that candidate routing improvements are live only when under test, that none is currently active, and that it returns the same response shape as the stable router. It does not contradict annotations and gives useful context beyond the structured hints.

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 a compact paragraph with essential context, front-loading the beta identity and equivalence to ask_pipeworx, then explaining the experimental routing behavior and current status. It is somewhat longer than strictly necessary but each sentence adds distinct information: identity, scope, behavior, usage instruction, and fallback guarantee. No redundancy.

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 no output schema, the description should explain what the agent can expect. It states the same response shape as ask_pipeworx, which is sufficient because the sibling tool presumably defines the shape. It also clarifies that it is fully functional and not a stub, preempting the main risk in beta tools: unreliability. The only minor gap is not explaining what routing improvements are or how results are compared, but that is not necessary for invoking the tool, and the explicit equivalence to ask_pipeworx covers usage expectations.

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 description of the question parameter and its aliases fully documented. The tool description adds no additional semantics beyond the schema because it explicitly says same arguments as ask_pipeworx, but the schema already covers all six parameters comprehensively. A baseline 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 this as the beta variant of ask_pipeworx—a universal router with the same tool set, arguments, and response shape as the stable sibling—while specifying the current status (no active candidate, currently identical to ask_pipeworx). It distinguishes itself from ask_pipeworx by positioning as the experimental edge for comparing routing improvements.

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 explicitly states when to use it: 'Use it exactly like ask_pipeworx when you want the newest routing.' It explains the purpose of results comparison against the stable router and explicitly warns it falls back to nothing, meaning it is a full working router rather than a stub. This provides clear context and disambiguates from sibling 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
Disambiguation3/5

The toolset is mostly organized by clear subdomains, but there are multiple overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer research questions and the beta version is currently identical to the stable router. Detailed descriptions reduce confusion, but an agent could still reasonably pick the wrong one for a given task. The entity, memory, and subscription tools are more clearly separated.

Naming Consistency3/5

Names are consistently lower_snake_case and readable, but the set mixes verb-led names (compare_entities, resolve_entity, validate_claim) with noun-led names (entity_profile, polymarket_edges, pipeworx_trending) and some odd pairings like ai_visibility_check vs scan_competitor_ai_presence. No chaotic camelCase or inconsistent separators, but the convention is not uniform enough for a strong score.

Tool Count2/5

34 tools is past the 25+ threshold and the surface spans many unrelated domains: structured data lookup, prediction markets, AI visibility marketing, city open data, npm dependency checking, llms.txt generation, memory, and subscriptions. Each tool may be individually useful, but the collection feels like a platform dump rather than a tightly scoped server. A more focused server would split off prediction markets, AI visibility, and utility tools.

Completeness4/5

The main data-research workflow is well covered: discovery, routing, grounded answering, deep research, entity resolution, profiles, comparisons, recent changes, claim validation, and search-within-results are all present. Prediction-market analysis, memory, and subscription lifecycles also have no major dead ends. Minor gaps exist, such as no write/update path for open data and no subscription option for AI-visibility monitoring, but these are not central to the apparent core purpose.