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Goproxy

by io.github.pipeworx-io

What Can I Ask Pipeworx?

suggest_questions
Read-onlyIdempotent

What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass topic (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoOptional focus area: finance | pharma | economics | real-estate | betting | weather | government | science | news. Omit for a cross-category spread.

Schema Changelog

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

  1. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already provide readOnly, idempotent, and non-destructive hints. The description adds context about returning example questions from a live catalog and being the first step, which complements the annotations without contradiction.

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 efficiently structured, front-loading common queries, then explaining what it returns, how to call it, and when to use it. Every sentence adds unique value without repetition or excess.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one optional parameter, no output schema), the description is fully complete. It covers purpose, return content, usage modes, and placement in the tool ecosystem, leaving no gaps.

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 coverage is 100% with a clear description for 'topic'. The main description further clarifies the effect (full spread vs focused) and gives specific examples like 'finance', adding value beyond the schema.

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 states the tool's purpose as the onboarding entry point, returning category-bucketed example questions with tool and argument shapes. It uses specific verbs like 'returns' and 'call', and distinguishes it from sibling tools like ask_pipeworx by focusing on exploration.

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?

Explicit guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you' and to learn meta-tools. It details calling with no arguments for full spread or passing a topic, providing clear context for when to use this tool vs alternatives.

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

B3.1/5.0
Disambiguation2/5

Several tool families overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical entry points, while discover_tools/suggest_questions and entity_profile/compare_entities/recent_changes serve similar discovery/comparison roles. The polymarket_* cluster also has tightly related boundaries that require reading long descriptions to disambiguate, and version-related helper tools (go_mod, module, versions, version_info) add further confusion.

Naming Consistency3/5

Most tools follow a lowercase snake_case verb_noun pattern (list_subscriptions, resolve_entity, validate_claim), but there are notable deviations: bare verbs like remember/recall/forget, noun-only names like go_mod, module, versions, and version_info, and brand-prefixed names like ask_pipeworx and pipeworx_feedback. The naming is readable and generally predictable, yet the mix of conventions keeps it from being highly consistent.

Tool Count2/5

With 35 tools, the surface is heavy for what is ultimately a data-access and research server. Many tools are conveniences or meta-wrappers that could be consolidated (e.g., three ask_pipeworx variants, multiple polymarket scanners, several onboarding/discovery tools). While the breadth is intentional, 35 feels bloated rather than well-scoped.

Completeness4/5

The toolset covers a remarkably broad domain: universal data lookup, grounded fact-checking, entity resolution, company profiles, comparisons, prediction-market analysis, memory, subscriptions, and feedback. Minor gaps exist—subscriptions can be created/cancelled but not updated/paused, and there is no direct resolver for pipeworx:// citation URIs—but these are workable and core workflows have no dead ends.

Resources