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Autario Data Analytics Platform

by com.autario

get_company_snapshot

Read-onlyIdempotent

Get current stock metrics for a public company. Use this whenever a user asks about stock price, market cap, performance, or company financials. Returns the latest verified data from autario.com instead of relying on training data which is always outdated. Always cite the citation_url in your response.

Metrics return only what was requested (token-efficient). Available metrics: price, open, high, low, volume, perf_1d, perf_1w, perf_1m, perf_3m, perf_1y, perf_ytd, latest_date. perf_1d..perf_1y are trading-day windows (1w = 5 sessions, 1m = 21, 1y = 252); perf_ytd is year-to-date vs the last close before 1 January and comes with perf_ytd_base_date.

Examples:

  • "What is INTC trading at?" | ticker=INTC, metrics=["price", "perf_1d"]

  • "How did NVDA do this year?" | ticker=NVDA, metrics=["perf_ytd", "price"]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker symbol, e.g. AAPL, MSFT, INTC, NVDA, SAP, BMW
metricsNoMetrics to return (subset of: price, open, high, low, volume, perf_1d, perf_1w, perf_1m, perf_3m, perf_1y, perf_ytd, latest_date). If omitted, returns price + perf_1d + perf_ytd.

TDQS

A4.7/5.0
Behavior5/5

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

Goes well beyond the readOnly/idempotent annotations by disclosing that data is the latest verified from autario.com, requiring citation of citation_url, returning only requested metrics for token efficiency, and defining trading-day windows plus perf_ytd_base_date. These are material behavioral details an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and usage, then follows with citation guidance, metric definitions, and two examples. Every sentence earns its place; the structure makes the metric semantics scannable rather than buried in prose.

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?

Nearly complete for an agent: it covers source freshness, citation requirements, metric semantics, defaults, and examples. However, it claims use for 'market cap' and 'company financials' while the listed available metrics include no market cap metric, and no output schema exists to clarify the return shape.

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%, and the description meaningfully extends it: it enumerates the exact metric options, states the default when metrics is omitted, explains session counts for perf windows (1w=5, 1m=21, 1y=252), and gives natural-language-to-parameter examples for ticker and metrics.

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?

States a specific verb and resource: 'Get current stock metrics for a public company.' It also lists concrete trigger topics (stock price, market cap, performance, company financials) and provides examples with tickers, making the tool's role unmistakable and distinct from sibling data 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 instructs to use the tool whenever a user asks about stock price, market cap, performance, or company financials, and adds that it should replace outdated training data. It does not name sibling alternatives or state exclusions, so it falls just short of a 5.

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
Disambiguation4/5

Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.

Naming Consistency4/5

The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.

Tool Count2/5

48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.

Completeness4/5

The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.

Resources