Get a cross-domain view of everything on the user's plate. Shows commitments from all life domains + promoted Playbooks from all workspaces, grouped by urgency. Use when the user asks "what should I focus on?", "what's on my plate?", "am I dropping anything?", or similar portfolio-level questions.
| Name | Required | Description | Default |
|---|---|---|---|
| companyId | No | FreedomOS company id to act within (you must be a member). Required for company-scoped tools. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The description indicates it 'shows' data (implying a read-only operation) and mentions the grouping criteria, but it does not disclose any side effects, permissions, or prerequisites (e.g., that a companyId may be required). It also doesn't clarify response structure or pagination. Given the absence of annotations, a fully self-sufficient description would need to state that this is a read-only aggregation and note any performance or auth considerations. The current description is adequate but not thorough.
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 two sentences: the first delivers the core functionality and scope, the second provides concrete usage examples. It is front-loaded, free of filler, and every sentence earns its place. Ideal conciseness for an MCP tool description.
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?
Given the tool's complexity (aggregating across domains and workspaces) and the absence of an output schema, the description does a good job of specifying what data is included (commitments, promoted Playbooks) and how it's grouped (urgency). It doesn't describe the exact output fields, but since there's no output schema, the description should at least convey the shape—it does. It also mentions the optional companyId in the schema, which is sufficient. Minor gap: it doesn't state whether the result includes tasks, objectives, or other elements beyond commitments and playbooks, but 'everything on the user's plate' plus the explicit list covers it reasonably.
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% (only one optional parameter, companyId, with a description). The tool description does not mention this parameter at all, but the schema itself explains it: 'FreedomOS company id to act within (you must be a member). Required for company-scoped tools.' Since the schema already carries the full semantic load, the description adds no additional value for parameters. Baseline 3 is appropriate.
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 states the tool's purpose: 'Get a cross-domain view of everything on the user's plate.' It specifies the contents (commitments from all life domains + promoted Playbooks from all workspaces) and the organizational principle (grouped by urgency). This distinguishes it from siblings like get_next_priority (likely single-priority) or list_my_work (likely a flat list). The verb 'get' plus the resource 'executive landscape' is specific and unambiguous.
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 explicitly names the use case: 'Use when the user asks "what should I focus on?", "what's on my plate?", "am I dropping anything?", or similar portfolio-level questions.' This gives clear contextual triggers. However, it does not mention alternatives or specify when NOT to use it (e.g., if the user wants a single commitment or a workspace-specific view). Still, the 'portfolio-level' qualifier helps an agent decide between this and more scoped tools.
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.