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campaignstack_update_competitor_watch_accounts

Idempotent

Replaces the LinkedIn accounts a competitor watch reads and acts with. Updates the bound signal agent's read and act pools, its workflow, and the discovery pool. Requires at least one account. Use campaignstack_list_accounts to find valid account IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
watchIdYes
accountIdsYesLinkedIn account IDs the watch reads and acts with. Replaces the current selection. Use campaignstack_list_accounts to find valid IDs.

Schema Changelog

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

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint false, idempotentHint true, destructiveHint false), the description explains exactly what is mutated: the signal agent's read and act pools, its workflow, and the discovery pool. It also makes the replacement semantics explicit. This gives an agent a solid behavioral model of the side effects.

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?

Three tight sentences with no filler. The primary action is front-loaded, followed by the impacted components and the required prerequisite. Every sentence adds useful information.

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?

For a two-parameter mutation tool with no output schema, the description is largely complete: it states the effect, the prerequisite, and the source for valid account IDs. It does not define watchId explicitly or mention edge cases, but the tool name and context make the intent reasonably clear.

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

Parameters3/5

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

The schema already describes accountIds well, and the description reinforces it by framing the action as replacing the accounts and requiring at least one. However, with 50% schema coverage, watchId has no schema description and the description also does not elaborate on what a watchId is or how to obtain one. The provided guidance is valid but only partially compensates for that gap.

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 opens with a specific verb and resource: 'Replaces the LinkedIn accounts a competitor watch reads and acts with.' It then names the concrete affected components (read/act pools, workflow, discovery pool), which clearly distinguishes it from related sibling tools like update_signal_agent or set_workflow_accounts.

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 makes the usage context clear: change which LinkedIn accounts a competitor watch uses. It also gives an explicit prerequisite and helper tool: 'Use campaignstack_list_accounts to find valid account IDs,' and states the minimum input requirement. It does not explicitly mention when not to use the tool, so it stops short of a 5.

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