List the workspace's lead signal watches with lead name, state (active/paused), why a paused watch is paused (user vs lead unreachable), last check and next check. Filter by state with the state argument. Related: campaignstack_create_signal_watch, campaignstack_pause_signal_watch, campaignstack_remove_signal_watch.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | all | |
| 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 and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral detail beyond that: it reports why a paused watch is paused, distinguishing user vs lead unreachable, and notes last/next check times. It does not mention pagination, but that is a minor gap for a simple list tool.
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 compact sentences front-load the core action and output fields, then cover filtering and related tools. Every sentence adds usable information with no repetition or fluff.
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 list tool with simple parameters and safety annotations, the description is largely complete: it identifies the resource, enumerates return fields, and explains the filter. There is no output schema, but the description compensates by describing expected fields. Pagination or response envelope details would be nice but are not essential.
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 carry parameter meaning. It explicitly explains the state argument as a filter and the 'workspace's' phrasing implies the workspaceId scope. It does not detail workspaceId formats, but the schema already requires it and its role is intuitive.
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 clearly states the tool 'List the workspace's lead signal watches' with a specific verb and resource, and enumerates the returned fields. This differentiates it from siblings like campaignstack_list_signal_agents and campaignstack_list_lead_signals by focusing specifically on signal watches.
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 gives clear context for when to use the tool, including the state filter, and lists related tools (create, pause, remove signal watch). It does not explicitly contrast with an alternative list tool, but the resource scope and related operations make the intended usage clear.
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.
