Get the current AI credit balance for a workspace. Returns monthly usage, included credits, rolled-over credits, pack balance, overage used, total pool size, remaining credits, percent used, and plan type.
| Name | Required | Description | Default |
|---|---|---|---|
| workspaceId | No | Defaults to the API key's workspace |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds value by disclosing exactly what is returned (monthly usage, included credits, rolled-over credits, pack balance, overage used, total pool size, remaining credits, percent used, and plan type), which is especially useful without an output schema.
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 with no filler. The core purpose is front-loaded, and the detailed return-field list is presented compactly as a single sentence.
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 getter with one optional parameter, annotations covering safety, and no output schema, the description is complete: it states what is retrieved and fully lists the response contents. No additional context is needed for an agent to call this 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?
Schema coverage is 100%, and the single optional workspaceId parameter already has a clear description: 'Defaults to the API key's workspace.' The tool description adds no substantive parameter meaning beyond confirming that the balance is scoped to a workspace, so the schema is carrying the load.
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 uses a specific verb ('Get') and a clear resource ('current AI credit balance for a workspace'), then enumerates the exact data returned. This makes the tool's purpose unambiguous and naturally distinguishes it from other get_* tools like get_workspace_billing or get_account.
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 intended use is clearly stated: retrieve the AI credit balance for a workspace. It does not explicitly list exclusions or alternative tools, but the highly specific resource and returned fields give an agent enough context to know when to call 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.
Many tools share the same verb prefix (create_, list_, update_, get_) across closely related resources, so pairs like add_lead_to_external_list vs add_lead_to_sequence, create_signal_agent vs create_signal_watch, and approve_review vs approve_content_post can be confused. The descriptions are unusually detailed and cross-referenced, which mitigates but does not eliminate the ambiguity inherent in a 282-tool surface.
Virtually every tool follows the campaignstack_verb_noun snake_case pattern, which is highly predictable. Minor deviations exist: destructive operations mix remove_ and delete_ (remove_lead_list vs delete_campaign), AI generation uses both craft_ and generate_, and the seo_/search_console_ subdomains introduce a second prefix convention.
282 tools is an extreme mismatch by any reasonable standard, exceeding the 50+ threshold by more than 5x. Even for a full B2B outreach platform, this surface is far too large and would be better consolidated into higher-level operations or grouped sub-servers.
The tool surface is impressively comprehensive, covering campaigns, workflows, leads, content, ads, SEO, integrations, billing, and more with CRUD-level depth. Minor gaps remain: no single-ICP getter, no direct pause/delete for search watches, and no explicit delete for ad campaigns (only archive via update).
