List all scheduled reports for this company, optionally filtered by agent. Use when user asks "what reports are scheduled?", "show me our reports", "what reports does X have?"
| Name | Required | Description | Default |
|---|---|---|---|
| agent_id | No | Optional: filter reports by a specific agent UUID | |
| companyId | Yes | 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?
Discloses the read-only nature (listing) and scope (company-wide, optional agent filter). Without annotations, it adequately covers behavioral expectations, though no mention of pagination or limits.
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 concise sentences front-loading the core purpose immediately, with zero extraneous information.
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 list tool with two parameters and no output schema, the description provides sufficient context about scope and optional filtering, enabling correct invocation.
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%, so baseline is 3. The description only paraphrases the agent_id parameter's filter role and omits companyId details already in schema, adding no new meaning.
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 the action ('List'), the resource ('scheduled reports'), and the scope ('for this company, optionally filtered by agent'). Distinguishes from sibling tools like list_attention_directives and list_commitments by specifying 'scheduled reports'.
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 explicit natural language examples ('what reports are scheduled?', 'show me our reports', 'what reports does X have?') that directly map to the tool's functionality, helping the agent recognize when to invoke it.
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.