Get the total number of unread conversations across all LinkedIn accounts in the workspace. Returns { total, accountCount } where total is the sum of unread conversation counts, accountCount is the number of active LinkedIn accounts in the workspace. Uses denormalized counters for efficiency (no conversation table scan).
| Name | Required | Description | Default |
|---|---|---|---|
| workspaceId | No |
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 establish readOnlyHint, idempotentHint, and non-destructive behavior. The description adds meaningful behavioral context: it returns a specific shape, defines accountCount as active accounts, and explains the use of denormalized counters for efficiency with no conversation table scan. This helps the agent understand performance and semantics beyond 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?
The description is three sentences with no filler. The main action is front-loaded, the return contract is stated precisely in the second sentence, and the efficiency note in the third sentence is a useful, non-redundant addition. 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?
The return value is fully described even though no output schema exists, and the workspace-scoped aggregation is clear. For a simple, read-only counter tool with strong annotations and one obvious parameter, this is nearly complete; the only minor gap is explicit parameter-level guidance.
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 schema has only one parameter, workspaceId, but no schema-level description (0% coverage), so the tool description carries the burden. The phrase 'in the workspace' connects the parameter to the count's scope but does not explicitly name workspaceId, explain its role, or clarify whether it is required. Partial compensation only.
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 opens with a specific verb and resource: 'Get the total number of unread conversations across all LinkedIn accounts in the workspace.' It clearly states the aggregated scope and distinguishes this aggregate-count tool from sibling tools like list_inbox_conversations or count_unread_notifications.
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 implied use case is clear: use this when the agent needs an aggregate unread-conversation count across all accounts rather than a list of conversations. However, there is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named to help disambiguate from nearby list/count tools.
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.
