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Unquant

Get company fundamentals

market_fundamentals
Read-onlyIdempotent

Return reported financial statements, ratios, and metrics for one company by quarterly or annual period.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoThe maximum number of results to return.
periodNoThe reporting period for each financial statement.quarter
symbolYesThe ticker symbol. Example: AAPL.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
metaYes
billingYes
warningsYes

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, giving the agent a strong safety profile. The description adds that it returns 'reported financial statements' (implying historical/actual data rather than projections) and supports quarterly/annual periods. However, it doesn't disclose output volume limits beyond the schema 'limit' param or describe pagination/response format behavior.

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 a single efficient sentence that communicates the core purpose. It's appropriately concise without wasted words, though it could slightly expand on scope or usage guidance without becoming verbose.

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

Completeness3/5

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

The tool has an output schema present and full schema parameter coverage, so much of the context is handled structurally. The description covers the core intent (financial statements by period). However, given the existence of many market_* sibling tools, it could better situate itself—e.g., noting it reports fundamental/accounting data as opposed to market prices or analyst views, which would improve completeness.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all three parameters (symbol, period, limit) with descriptions and enums. The description doesn't add meaningful semantics beyond what's already in the schema. Baseline 3 is appropriate since the schema carries the burden well.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb+resource: 'Return reported financial statements, ratios, and metrics for one company by quarterly or annual period.' It clearly specifies what is returned and the periodicity. It's a solid purpose statement, though it could better distinguish itself from siblings like market_company_profile or market_earnings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives like market_quote, market_earnings, or market_price_history. It doesn't mention prerequisites, whether ratings/analyst data is included (which market_analyst_ratings handles), or how it relates to its siblings. No when/when-not guidance given.

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
Disambiguation5/5

Each tool targets a distinct resource-action combination (catalog items, datasets, macro data, market data, news, politics, account). Even within the market_ prefix, tools are clearly separated by resource type (quote, fundamentals, earnings, ratings, profile). No two tools appear to perform the same operation.

Naming Consistency4/5

The naming follows a consistent noun_verb pattern with domain prefixes: catalog_, datasets_, macro_, market_, news_, politics_. The verb style is consistent (describe, list, search, request, submit, return-type verbs like indicator and quote). Slight deviation with account_request_upgrade vs account_upgrade_status, and some verbs double as noun forms (quote, indicator, preview), but overall the convention is predictable.

Tool Count4/5

At 28 tools, the count is on the high side, but it serves a broad data platform spanning seven distinct domains (catalog, datasets, macro, market, news, politics, account). Each domain earns multiple tools to cover its surface, and the domains are broad enough to justify the volume. Slightly heavy, but reasonable given the scope.

Completeness4/5

The surface covers the full discovery-to-delivery workflow for data: list, describe, preview, request (catalog), plus direct dataset access. Market data has symbols search, quotes, price history, fundamentals, earnings, ratings, ETFs, and profile. Minor gaps include no bulk quote or multi-ticker endpoints, and there's no tool for reading an existing catalog request's status, but core workflows are well-covered.

Resources