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Glama

Protein Atlas

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, covering safety. The description adds behavioral details: returns example questions with exact tool+argument shapes, drawn from a live catalog. 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 somewhat lengthy but each sentence contributes useful information. It front-loads the purpose and provides a clear structure. Could be slightly trimmed, but it's efficient for an onboarding tool.

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?

For a simple tool with one optional parameter and no output schema, the description is comprehensive. It covers when to use, how to use, what it returns, and its role in the ecosystem.

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%, but the tool description adds significant value by listing possible values for the topic parameter and explaining behavior when omitted. This goes beyond the schema's brief 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's purpose: returning category-bucketed example questions to help users learn what to ask Pipeworx. It uses specific verbs like 'returns' and 'use this FIRST', and distinguishes it from siblings by positioning it as the onboarding entry point.

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?

Explicitly says to use this tool first when unsure about Pipeworx's capabilities, and explains how to call with no arguments for a full spread or with a topic for focus. It also mentions learning how to call meta-tools, providing clear context.

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

All Protein Atlas toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), ask_pipeworx_grounded, deep_research, and validate_claim all handle routed research queries, while ai_visibility_check and scan_competitor_ai_presence overlap directly and the six Polymarket tools form a dense, easily confused cluster. The descriptions are detailed, but an agent will frequently struggle to pick the right tool among near-duplicate research and prediction-market options.

Naming Consistency3/5

Names are readable and mostly snake_case, with useful prefixes like ask_pipeworx_ and polymarket_. However, conventions are mixed: some are verb_noun (search_genes, get_protein, generate_llms_txt), some are bare verbs (remember, recall, forget), and some are noun phrases (entity_profile, recent_changes, top_tissues). There is no single predictable pattern.

Tool Count2/5

34 tools is above the 25+ threshold for a heavy, hard-to-navigate set, and most of them are not related to the server's stated 'Protein Atlas' identity. Only three tools actually concern proteins, while the rest form a general data-research, Polymarket, memory, and subscription toolkit that feels like several servers merged into one.

Completeness2/5

For a Protein Atlas server, the surface is severely incomplete: only search_genes, get_protein, and top_tissues cover HPA, leaving pathology, cell-line, single-cell, blood, and other major HPA dimensions unaddressed. If the intended domain is instead the broader Pipeworx data router, the protein tools are an odd vestige and the completeness story is still muddled by overlapping meta-tools.

Resources