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Glama

campaignstack

by io.campaignstack

campaignstack_create_signal_watch

Idempotent

Watch a lead for profile signal changes: the system refreshes their profile daily and the workspace gets an alert (in-app + email) when a monitored field changes (title, company, seniority, headline, location). Idempotent: watching an already-watched lead returns the existing watch. Active watches are capped per workspace. Use campaignstack_search_leads to find lead IDs and campaignstack_list_signal_watches to see current watches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leadIdYes
leadSourceNoglobal
workspaceIdYes

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

The description significantly expands on the annotations by disclosing the daily refresh cadence, alert channels, monitored fields, idempotent return behavior, and the per-workspace cap on active watches. No contradiction with annotations exists.

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, front-loaded with the core behavior, and every sentence adds value: monitoring behavior, idempotence, cap, and prerequisite tools. No filler or redundancy.

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?

The description covers the purpose, monitoring behavior, idempotence, cap, and discovery workflow, which is strong for a tool without an output schema. The main omission is leadSource semantics and perhaps explicit cleanup/removal guidance, so it falls just short of fully complete.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not explain the leadSource parameter at all. While leadId and workspaceId are inferable from context, leadSource values ('global'/'private') remain undefined, leaving a real gap 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 states a specific verb and resource: watch a lead for profile signal changes. It explains exactly what happens (daily refresh, alerts on title, company, seniority, headline, location), which distinguishes it from related watch/agent tools.

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 provides clear workflow context by pointing to campaignstack_search_leads for finding lead IDs and campaignstack_list_signal_watches for current watches. It does not explicitly compare to alternative watch tools, but the usage context is clear enough.

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

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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