campaignstack_count_pending_reviews
Count pending workflow review actions in a workspace. Returns the total number of entries awaiting human approval.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| workspaceId | Yes |
Count pending workflow review actions in a workspace. Returns the total number of entries awaiting human approval.
| Name | Required | Description | Default |
|---|---|---|---|
| workspaceId | Yes |
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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context beyond those annotations by specifying exactly what is counted ('entries awaiting human approval') and what the return value is (a total number). This is especially valuable because there is no 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?
The description is one efficient sentence with no filler. It front-loads the action ('Count'), states the resource, and immediately clarifies the return value. Every clause 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 simple count operation with one parameter, the description is complete: it states the scope, the subject, and the return format. Annotations cover the safety profile, so no further behavioral caveats are necessary. The absence of an output schema is adequately compensated by explicitly stating the return value.
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 0%, so the description must compensate for the workspaceId parameter. The phrase 'in a workspace' does add some meaning by indicating that workspaceId scopes the count, but it does not explain where to obtain the ID or any additional constraints beyond the schema's minLength. The single self-explanatory parameter keeps this adequate but not strong.
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 ('Count'), names a precise resource ('pending workflow review actions'), and scopes it to a workspace. It also states the exact return behavior ('Returns the total number of entries awaiting human approval'), which clearly distinguishes it from sibling tools like list_pending_reviews.
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 implied by the verb and return value: call this when you need a count of pending review actions rather than a detailed list. However, there is no explicit mention of the natural alternative (campaignstack_list_pending_reviews) or any when-not-to-use guidance, so the agent must infer the distinction.
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 set is enormous and generally well-differentiated through detailed cross-referenced descriptions, but several clusters blur together: archive/delete/remove have inconsistent permanence semantics (delete_campaign vs remove_signal_watch vs archive_campaign), create_connection_watch_agent explicitly overlaps with set_account_watcher, and the parallel draft-checkup and playbook-proposal flows (run_draft_checkup/get_draft_checkup/accept_draft_checkup vs propose_playbook_change/get_playbook_proposal/decide_playbook_proposal) present near-identical decision pipelines.
Nearly every tool follows the campaignstack_<verb>_<noun> convention with disciplined get/list pairing and consistent verb choices (create/update/delete/pause/resume). Minor deviations like campaignstack_priority_enrich (adverb+verb) and campaignstack_whoami break the strict verb_noun pattern but are isolated and do not hinder navigation.
223 tools is an extreme surface for any MCP server. Even though each tool maps to a distinct API operation and the underlying platform is broad, the scale far exceeds the 50+ threshold for an extreme mismatch and will overwhelm agents with selection overhead.
The surface is exhaustive for the LinkedIn outreach domain: full campaign/workflow/lead-list lifecycles, ICP and persona management, content scheduling and approvals, inbox and messaging, enrichment and integrations, signal watches and exclusions, review queues, playbook versioning, workspace admin, billing, and notifications. Minor gaps like a missing delete_lead or delete_company are explained by shared-data semantics, so no critical dead ends remain.