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Data Sncf

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

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint and idempotentHint. Description adds behavioral detail: returns example questions with exact tool+argument shape, drawn from live catalog. No contradictions. Adds value beyond 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?

Description is well-structured with front-loaded trigger phrases and parentheses. Dense but every sentence adds value. Slightly long but appropriate for the complexity.

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 explains return format clearly. Covers all necessary context: when to use, what it returns, how to call, and examples. Complete for the tool's purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with description for topic parameter. Description adds context: 'Omit for a cross-category spread', lists example topics, and explains behavior with and without argument. Highly enhances 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 it is an onboarding entry point that returns category-bucketed example questions with exact tool calls. It distinguishes from siblings by stating 'Use this FIRST' and listing what it covers, differentiating it from specialized tools like ask_pipeworx or entity_profile.

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?

Explicit usage guidance: 'Use this FIRST when you do not yet know what Pipeworx can do for you' and 'or to learn how to call the meta-tools'. It does not explicitly state when not to use, but the 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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes, particularly the ask_pipeworx variants (standard, beta, grounded) and the Polymarket analysis tools (bet_research, polymarket_edges, polymarket_arbitrage). While descriptions help differentiate, an agent may still select the wrong tool for a given task.

Naming Consistency3/5

Names mix verb-initial (ask_pipeworx, compare_entities) and noun-initial (dataset_info, pipeworx_feedback, polymarket_arbitrage) patterns. The snake_case convention is consistent, but the lack of a uniform verb_noun pattern reduces predictability.

Tool Count3/5

With 34 tools, the server is overloaded relative to a clear scope. Many tools are meta-tools (memory, subscription management, feedback) that inflate the count. A more focused set of 15-20 tools would be more coherent.

Completeness4/5

The tool surface covers a wide range of domains: structured data queries, entity profiles, comparisons, prediction market analysis, memory, subscriptions, and SNCF-specific data. Minor gaps exist (e.g., no dedicated weather or sports tools), but the universal ask_pipeworx compensates. Overall, users can accomplish most tasks without dead ends.

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