Get analytics for a workspace's own published LinkedIn content. Returns engagement metrics (reactions, comments, reposts), trend vs previous period, and top-performing posts. Use fromMs/toMs as Unix timestamps (milliseconds) to define the date range.
| Name | Required | Description | Default |
|---|---|---|---|
| toMs | Yes | ||
| fromMs | Yes | ||
| workspaceId | No | Workspace ID (required for user keys; workspace keys are bound) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / workspaceId / descriptionPrevious value: -"Defaults to the API key's workspace"New value: +"Workspace ID (required for user keys; workspace keys are bound)"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the safety profile is covered. The description adds useful behavioral context by listing the returned data: engagement metrics, trend vs previous period, and top-performing posts, which goes 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?
Three sentences, front-loaded with purpose, followed by return-value summary and parameter guidance. Every sentence contributes meaningful information with no wasted words.
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 read-only analytics tool with two required parameters, the description is complete: it explains scope, inputs, and key outputs. Although there is no output schema, the description provides enough detail about the return content for an agent to know what to expect.
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 only 33%, but the description directly compensates by explaining that fromMs/toMs are Unix timestamps in milliseconds used to define the date range. The optional workspaceId is already described in the schema with its key-scoping nuance.
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 states a specific action ('Get analytics') and a specific resource ('a workspace's own published LinkedIn content'), making it clear what the tool operates on. This also distinguishes it from sibling analytics tools like campaign metrics or overall workspace metrics.
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?
It clearly states the target scope ('workspace's own published LinkedIn content') and explains the date range mechanism using fromMs/toMs. It does not name alternatives or explicitly exclude other tools, but the context is clear enough for an agent to select this tool appropriately.
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.