campaignstack_resume_competitor_watch
Resumes a paused competitor watch and re-enables its associated automations. Use campaignstack_pause_competitor_watch to pause monitoring.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| watchId | Yes |
Resumes a paused competitor watch and re-enables its associated automations. Use campaignstack_pause_competitor_watch to pause monitoring.
| Name | Required | Description | Default |
|---|---|---|---|
| watchId | 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 indicate readOnlyHint=false, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context by disclosing that resuming also 're-enables its associated automations,' which is a non-obvious side effect. It does not describe failure states, but the idempotency annotation partially covers repeated-call safety.
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 short sentences carry the essential information: what the tool does, what side effect it has, and which sibling handles the inverse operation. There is no filler or repetition of the schema.
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 one-parameter mutation with no output schema, the description plus annotations cover purpose, side effects, idempotency, and the counterpart tool. The main gap is that watchId semantics are only implied rather than explicitly defined, and there is no mention of what happens if the watch is not currently paused.
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 only defines watchId as a string with minLength 1, and schema description coverage is 0%, so the description needed to clarify the parameter. It does not explicitly state that watchId is the identifier of the paused competitor watch. However, the tool name and the phrase 'Resumes a paused competitor watch' make the single parameter's role reasonably inferable.
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 ('Resumes') with a clear resource ('a paused competitor watch') and states the concrete outcome ('re-enables its associated automations'). It also distinguishes this tool from its pause counterpart by naming campaignstack_pause_competitor_watch, so the agent can tell them apart.
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 explicitly points to the alternative operation: 'Use campaignstack_pause_competitor_watch to pause monitoring.' This gives useful context for when to use this tool vs. its inverse. It does not explicitly enumerate exclusions or other alternatives, but the resume/pause pairing is sufficient for the common case.
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.