Accept the pending draft checkup: applies the proposed text to its target (intent detail, playbook section, or offer context) through the same caps and version snapshots as a manual edit. Fails when the targeted text changed since the checkup ran; run a new checkup in that case.
| Name | Required | Description | Default |
|---|---|---|---|
| checkupId | Yes | Pending checkup id from campaignstack_get_draft_checkup | |
| 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 this is a non-read-only, non-idempotent operation. The description adds valuable behavioral context beyond those annotations: it applies text 'through the same caps and version snapshots as a manual edit' and fails on stale targets. This gives the agent a concrete model of side effects without contradicting the annotations.
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 carry a complete behavioral contract: what the tool does, how it behaves relative to a manual edit, and the key failure condition with remediation. Every clause earns its place and the most important verb is front-loaded.
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?
The tool has no output schema, but it is an action rather than a query, so return-value documentation is less critical. The description covers the action, the mutation semantics, and the failure mode. It could be slightly more explicit about the success response, but the essential context for correct invocation is present.
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%, and the checkupId parameter is already well documented as coming from campaignstack_get_draft_checkup. The description adds no additional parameter-level meaning, so the baseline of 3 applies.
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?
States a specific verb ('Accept') and resource ('pending draft checkup'), and further explains the action by saying it applies proposed text to a target. It distinguishes itself from sibling tools like reject_draft_checkup and run_draft_checkup by describing the commit-like behavior.
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 clearly indicates when to use the tool: when a draft checkup is pending and should be accepted. It also provides an important exclusion cue by stating it 'fails when the targeted text changed since the checkup ran' and instructs to 'run a new checkup in that case,' which effectively steers an agent away from this tool under stale conditions.
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).
