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Expression 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.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds behavioral context: returns example questions with tool+argument shape, supports topic filtering, and is safe for repeated calls. No contradiction with annotations.

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 relatively long but well-structured: starts with example queries, explains purpose, describes output, then usage guidance. It's front-loaded with the most critical information. Could be slightly more concise, but 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?

Given the tool's simplicity (one optional param, no output schema), the description is remarkably complete. It covers when to use, what it returns, how to invoke it, and even lists categories. No gaps remain for an AI agent to be uncertain.

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% (one optional param with description). The description adds meaning by listing possible topic values and explaining the effect of omitting vs. including the parameter, going beyond the schema's minimal 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: an onboarding entry point that returns category-bucketed example questions about Pipeworx capabilities. It uses specific verbs ('returns', 'learn how to call') and distinguishes it from sibling tools by positioning it as the first tool to use when unfamiliar with Pipeworx.

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?

The description explicitly tells when to use this tool ('Use this FIRST when you do not yet know what Pipeworx can do') and provides clear alternatives ('meta-tools') and guidance on parameters ('no arguments for full spread, or pass topic to focus').

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

A3.7/5.0
Disambiguation3/5

Many tools have overlapping purposes (e.g., multiple Polymarket tools, multiple company information tools), but descriptions help distinguish them. However, the variety of domains (brand monitoring, betting, package scanning, memory, etc.) can confuse an agent.

Naming Consistency2/5

Tool names have no consistent naming pattern: some are verb_noun (validate_claim), some are noun_verb (bet_research), some are compound (ai_visibility_check), and some are single word (forget). This makes it hard to predict tool names.

Tool Count2/5

28 tools is high, and the set spans many unrelated domains (brand visibility, SEC/FDA data, Polymarket, npm packages, memory, etc.) while the server is named 'Expression Atlas' with only 2 tools related to that purpose. The tool count feels excessive and unfocused.

Completeness1/5

For a server named 'Expression Atlas', only two tools (get_experiment, search_experiments) cover the domain. There are no tools for submitting, updating, or deleting experiments, nor any for data visualization. The surface is severely incomplete.

Resources