Get projected future values from financial forecasts. Shows what revenue/expenses are expected. Empty books return a structured empty object (has_data: false), not an error.
| Name | Required | Description | Default |
|---|---|---|---|
| companyId | Yes | FreedomOS company id to act within (you must be a member). Required for company-scoped tools. | |
| fiscal_year | No | The fiscal year to query (default: current year) | |
| account_name | No | Optional filter to specific account name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral disclosure burden. It adds a valuable edge-case behavior: empty books return a structured empty object with has_data: false rather than an error. This goes beyond the schema and helps agents handle empty results gracefully.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the primary action, and each sentence adds value. The empty-book behavior is an important addition and is stated without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only tool with fully documented parameters, the description covers the core purpose and an important edge case. It does not describe the full return shape for non-empty results, but since there is no output schema, the description gives enough for an agent to understand the expected data type.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the parameters are already well documented in the schema. The description does not add parameter-specific details such as fiscal_year default behavior or account_name filtering semantics beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as retrieving projected future financial values, specifically revenue and expenses. It distinguishes itself from siblings like get_actuals_vs_budget by emphasizing 'projected' and 'expected,' though it does not explicitly name an alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage: use this when you need expected future revenue/expense values from financial forecasts. It does not provide explicit when-to-use or when-not-to-use guidance, nor does it reference alternative tools for actuals or budget comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.