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Mydisease

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 indicate idempotent, read-only, non-destructive behavior. Description adds context of being an onboarding tool, and explains return format (category-bucketed example questions with tool calls). 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?

Description is relatively long but well-structured: starts with alternative phrasings, then purpose, output description, and usage note. Every sentence adds value.

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?

Despite no output schema, the description fully explains the return type (category-bucketed example questions with tool+argument shape). For a simple tool with one optional param and strong annotations, it is complete.

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%. Description expands on the optional 'topic' parameter by listing example values (finance, pharma, betting) and explaining behavior with and without it.

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 this is an onboarding entry point that returns example questions. It uses specific verbs like 'suggest questions' and differentiates from siblings like ask_pipeworx and discover_tools.

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 advises to use this FIRST when the agent doesn't know what Pipeworx can do. Mentions calling with no arguments for full spread or with topic for focus. Does not explicitly state when not to use, but context is clear.

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
Disambiguation2/5

Multiple research/query entry points overlap heavily: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research sit on the same routing core, and validate_claim/bet_research/entity_profile all wrap lookup-and-analyze behavior. The detailed descriptions help within specialized clusters, but the central ask_pipeworx family alone creates real selection ambiguity.

Naming Consistency3/5

All names are lower_snake_case and several families are consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the overall set mixes bare verbs, nouns, and verb_noun composites with no global pattern (disease, metadata, query, entity_profile, generate_llms_txt, validate_claim). It is readable but not predictable across the full 34-tool surface.

Tool Count2/5

34 tools is over the 25+ threshold and the set bundles several distinct domains—disease ontology, Pipeworx data access, prediction markets, AI visibility, npm scanning, memory, and subscriptions—into one server. Each subfamily may be justified, but the combined surface is heavy and makes tool selection harder than the underlying tasks require.

Completeness4/5

The disease domain has query/disease/metadata for search-and-fetch read coverage, and the broader research side has lookup, grounded verification, deep research, entity profiles, comparisons, subscriptions, and memory lifecycle tools. Minor gaps exist (no direct tool to fetch pipeworx:// citation URIs, no disease browsing/pagination), but these are workable rather than blocking.

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