List CRM deals for the current company. Filter by stage and limit. Returns deals with their associated contacts.
Routing: CRM/sales → see open pipeline → use this
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max number of deals to return (default 20, max 100) | |
| stage | No | Filter by stage (optional) | |
| companyId | Yes | FreedomOS company id to act within (you must be a member). Required for company-scoped tools. | |
| include_closed | No | Include closed deals (default false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It discloses that deals are returned with associated contacts but does not mention read-only nature, pagination, or side effects.
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?
Two sentences plus a routing hint. Every sentence adds meaningful information with no redundancy.
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 no output schema, the description explains what is returned (deals with contacts) and the filtering options. It is reasonably complete for a listing tool.
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 coverage is 100% with good descriptions per parameter. The description adds value by noting that returns include associated contacts, which is not in schema.
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?
Clearly states that the tool lists CRM deals for the current company with filtering options and return information. The verb 'list' and resource 'deals' are specific and distinct from siblings.
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?
Provides a routing hint ('CRM/sales → see open pipeline → use this') that gives context for when to use, but does not explicitly state when not to use or mention alternatives.
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.