get_active_spaceReport the MCP server's current active Space binding. Returns { active_space_id, source, name?, role?, type?, campaign_count? }. source is 'env' (set at start-up via GROWOMAT_SPACE_ID), 'set_active_space' (changed at runtime), or 'default' (unbound — the BE falls back to the caller's Personal Space). No API round-trip when nothing is bound.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description goes beyond by disclosing the source field values ('env', 'set_active_space', 'default'), the fallback to the caller's Personal Space, and the no-API-round-trip behavior when unbound. This is rich behavioral context.
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?
Three sentences with no fluff. The first sentence states the purpose, the second lists return fields and source values, and the third covers the no-round-trip edge case. Every sentence earns its place.
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 parameterless getter with no output schema, the description fully documents the return shape, source values, and the unbound fallback behavior. It is complete for an AI agent to invoke and interpret results correctly.
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?
The tool has zero parameters, so the schema already covers 100% of parameter meaning. The description does not need to compensate, and the baseline of 4 applies. It does explain the return object, which is relevant to the tool's semantics.
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 reports the MCP server's current active Space binding, with a specific verb+resource and a detailed return object. This distinguishes it from siblings like set_active_space and list_spaces.
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?
Clear context as a read-only getter for the active space, with source semantics explained. No explicit when-not or alternative mentioned, but the obvious counterpart is set_active_space, so the usage is implied.
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.
Most tools follow a clear resource+action pattern (get_ad, update_ad, delete_ad), so entity-level tools are easy to distinguish. The main ambiguity is among reporting/status tools like get_conversion_status, get_conversion_health, get_conversion_report, and account_summary vs. get_performance_stats, which could tempt misselection without reading descriptions.
Naming is highly consistent: nearly every resource has create_/get_/update_/delete_/list_ variants, with predictable special verbs like set_, trigger_, preview_, and upload_. Minor exceptions like account_summary or get_angle_readout still follow the same readable verb-driven style.
83 tools is an extreme surface for an MCP server, even one covering ad management. Most entities have full CRUD plus many custom readouts and report variants, which creates context bloat and makes tool selection unnecessarily expensive for the agent.
The platform covers the full campaign lifecycle: campaigns, ad groups, ads, keywords, assets, business/creative planes, conversion actions, sync, budget, and targeting are all represented. Minor gaps exist—conversion sources have create/list but no get/update/delete, and there is no explicit way to update or remove a conversion source—but these can be worked around.