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Data Sncf

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

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, idempotentHint, destructiveHint. The description adds details: uses latest 10-K data, handles off-calendar fiscal years, returns sorted results and citation URIs. 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?

Front-loaded with query examples, each sentence adds value. Slightly long but justified given the information density. Could be slightly trimmed without loss.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description explains it 'Returns paired data + pipeworx:// citation URIs per entity.' Covers data sources, sorting, and typical use cases. While exact output structure is vague, it is adequate for the tool's role.

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% but description adds significant meaning: explains what each type returns, acceptable value formats (tickers vs drug names), min/max items, and sort behavior. Goes well beyond 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 performs side-by-side comparison of 2-5 companies or drugs, with concrete query examples. It distinguishes from sequential single-entity lookups, implying it replaces multiple calls to entity_profile or similar 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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' Provides context on data sources per type but does not explicitly list sibling tools to avoid.

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.

Resources