Skip to main content
Glama

Opendosm My

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, etc., and the description adds behavioral context: returns category-bucketed, live-catalog examples, supports optional topic filtering, and is safe for initial discovery. No contradictions.

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 single paragraph with front-loaded key purpose. It lists all topics and explains return structure. Slightly verbose but every sentence is informative; could be tightened slightly without loss.

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?

No output schema, but description fully explains what is returned (category-bucketed example questions with tool+argument shapes). Covers both usage modes (no args vs. topic) and context for when to use. Complete for an onboarding tool.

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% (one parameter with description), but the description lists specific topic values (finance, pharma, etc.) and explains that omitting gives full spread, adding significant meaning beyond the schema's generic description.

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 is the onboarding entry point for 'what can I ask Pipeworx?', returns categorized example questions with tool+argument shapes, and distinguishes itself from sibling tools like ask_pipeworx or entity_profile by being the first-use discovery tool.

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?

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you', providing clear context. Does not explicitly state when not to use, but the guidance is sufficient for an AI agent to select this tool appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Opendosm My toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation3/5

The set includes three nearly-identical ask_pipeworx variants (base, beta, grounded) that differ only subtly, and deep_research overlaps with ask_pipeworx for multi-part queries. Several company/prediction tools also share adjacent purposes (entity_profile vs recent_changes vs compare_entities; polymarket_arbitrage vs polymarket_edges), though detailed descriptions help. Overall, an agent could mis-select between these overlapping tools.

Naming Consistency4/5

Most tools follow a verb_noun or consistent prefix pattern (opendosm_*, polymarket_*), and the ask_pipeworx family is internally consistent. However, a few are noun phrases (entity_profile, ai_visibility_check, recent_alerts) and some verbs aren't uniform (list_datasets vs dataset_meta vs get_dataset). The mixture is readable but not fully consistent.

Tool Count2/5

34 tools is well above the typical focused server range and includes a clearly redundant experimental variant (ask_pipeworx_beta) plus many loosely-related utility functions (memory, subscriptions, dependency scanning, llms.txt generation). While the broad scope justifies some size, the count feels excessive for the 'Opendosm My' name, which suggests a narrower Malaysian-statistics focus.

Completeness4/5

For the apparent overarching goal of a multi-domain research/query platform, the surface is quite complete: data lookup, deep research, entity comparison, claim verification, prediction-market analysis, memory, and subscriptions are all covered. The Malaysian OpenDOSM component itself has list/meta/get lifecycle. Minor gaps (e.g., no direct dataset search beyond curated lists, no way to execute arbitrary Pipeworx tools directly) are workable.

Resources