campaignstack

campaignstack_create_signal_agent

Create a new signal agent that monitors a LinkedIn profile, company feed, group, or post for engagements. Auto-generates a template workflow and lead list. target specifies what to watch: { platform: 'linkedin', kind: 'profile', urn: '...' } for profile/company feeds, { platform: 'linkedin', kind: 'group', url: '...', groupId: '...' } for groups, or { platform: 'linkedin', kind: 'post', url: '...', activityId: '...' } for specific posts. accountIds are the accounts that observe the feed; actAccountIds are the accounts that act on signals. Use campaignstack_list_accounts to find account IDs.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`name`	Yes
`type`	Yes
`target`	Yes
`accountIds`	Yes
`targetName`	Yes
`workspaceId`	No
`responseMode`	Yes
`silenceHours`	No
`actAccountIds`	Yes
`publicReplyConfig`	No
`followUpDelaysHours`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedSep 6, 2026
- addedInput schema / properties / followUpDelaysHours / maxItems
```
Added value: +10
```

Changed2 schema fields changedSep 4, 2026

addedInput schema / properties / followUpDelaysHours

Added value: +{
+  "items": {
+    "type": "number"
+  },
+  "type": "array"
+}

addedInput schema / properties / silenceHours

Added value: +{
+  "maximum": 720,
+  "minimum": 1,
+  "type": "integer"
+}

First observedSep 4, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds meaningful behavioral detail beyond the annotations, particularly that creating the agent also auto-generates a template workflow and lead list, and that accountIds observe while actAccountIds act on signals. It does not contradict the annotations, and the mutation implication of 'create' aligns with readOnlyHint=false.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact yet information-dense, front-loading the primary purpose before detailing target variants and account roles. Every sentence contributes value, with no repetition of schema constraints or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of the nested target object and 11 parameters, the description covers the essential decision points: what to monitor, how the target object differs by kind, and how accountIds/actAccountIds are used. It does not mention what the tool returns, but since no output schema exists and the description already explains the core creation behavior, this is a minor gap rather than a blocking one.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at 0%, the description carries the burden for parameter meaning and does so for the most complex fields: target shapes and the observer/actor distinction between accountIds and actAccountIds. It does not explain optional parameters like silenceHours, followUpDelaysHours, or publicReplyConfig, but the core required parameters are sufficiently clarified for correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Create a new signal agent'), the resource type, and the scope: monitoring a LinkedIn profile, company feed, group, or post for engagements. It also distinguishes this from similar watch tools by noting it auto-generates a template workflow and lead list, making the tool's purpose specific and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on what the tool is for and includes an explicit pointer to campaignstack_list_accounts for finding account IDs. However, it does not explicitly contrast this tool with sibling tools like create_signal_watch or create_search_watch, so an agent must infer when this signal-agent variant is preferred.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All campaignstack tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0

Disambiguation3/5

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.

Naming Consistency4/5

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.

Tool Count1/5

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.

Completeness5/5

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.

Resources

Need Help?