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get_financial_summary

Get P&L summary with revenue, expenses, and net income for the company. For single-month queries (e.g., "Feb free cash flow"), specify month parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
monthNoSpecific month (1-12). If provided, returns data for that month only. If omitted, uses period parameter for range.
periodNoTime period for summary when month is not specified (default: ytd)
companyIdYesFreedomOS company id to act within (you must be a member). Required for company-scoped tools.
fiscal_yearNoThe fiscal year to query (default: current year)

TDQS

A3.8/5.0
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It only states it 'gets' a summary, implying a read operation, but does not confirm read-only behavior, required permissions, or any potential side effects. For a financial data retrieval tool, more transparency (e.g., 'returns historical data, no modifications') is needed.

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 two sentences, front-loaded with the primary purpose, followed by a targeted usage example. No unnecessary words; each sentence earns its place.

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?

Given no output schema, the description hints at return values (revenue, expenses, net income) but doesn't specify structure or data types. It covers the main use case and parameter interplay adequately, but could mention that the result is a single summary object for completeness.

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 description coverage is 100%, so baseline is 3. The description adds value by giving a concrete example ('Feb free cash flow') and connecting the month parameter to single-month queries, which goes beyond the schema. It also hints at the behavior of month vs period without redundancy.

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 uses a specific verb ('Get') and identifies the resource ('P&L summary') with key components ('revenue, expenses, and net income'). It distinguishes itself from sibling financial tools by focusing on the company-level profit and loss summary, making its purpose clear and distinct.

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

Usage Guidelines3/5

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

The description provides limited usage guidance: it suggests using the month parameter for single-month queries. However, it does not explicitly state when to use this tool versus alternatives (e.g., get_actuals_vs_budget for variance analysis, get_cash_position for cash flow). It lacks exclusions or prerequisites.

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

A3.6/5.0
Disambiguation4/5

The tool set is heavily disambiguated by detailed routing descriptions, domain prefixes, and lifecycle verbs, so most tools have a clear intended purpose. However, at 297 tools there are still close pairs and overlapping decision surfaces (e.g., approval workflows, 'what should I work on' readers, multiple finance/ads readers) that require careful description reading to avoid misselection.

Naming Consistency4/5

Naming is predominantly consistent snake_case verb_noun with strong domain prefixes like shopify_, x_, posthog_, and list_/create_/update_ patterns. Minor inconsistencies exist, such as several collection-returning tools using get_ (get_team_members, get_icps, get_okrs) instead of list_, and some generate_ vs create_ vs draft_ verbs, but the pattern is still predictable overall.

Tool Count1/5

297 tools is an extreme outlier and far beyond a usable MCP tool surface. Even a large suite has no justification for this count in one server; the agent would struggle to select among hundreds of similarly descriptive tools, and the natural 3-15 tool range is exceeded by nearly 20x.

Completeness4/5

The individual domains represented — OKRs, CRM/leads, Shopify, content pipelines, ads, PostHog, team hiring, knowledge, finance, and session management — are covered remarkably well with full lifecycle patterns. Minor gaps exist, such as no full deal CRUD, no delete for several Google/Shopify artifacts, and some analytical surfaces being read-heavy, but most workflows can be completed without dead ends.

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