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campaignstack_refresh_inbox

Trigger an inbox refresh for all LinkedIn accounts in the workspace. Polls LinkedIn for new messages and routes them into CampaignStack conversations. Respects a 20-second freshness dedup window per account (skips recently-fetched accounts). Returns { refreshed, skipped, errors } where refreshed = accounts polled, skipped = fresh/expired/unenriched accounts. After refresh, use campaignstack_list_inbox_conversations to see updated conversations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceIdNo

Schema Changelog

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

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish that this is a mutating, non-idempotent, open-world operation. The description goes further by disclosing the 20-second freshness dedup window, the fact that recently-fetched accounts are skipped, and the exact return shape. This adds meaningful behavioral detail beyond the annotation flags.

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 dense sentences carry action, scope, dedup behavior, return format, and a follow-up recommendation. There is no filler, and the most important action is front-loaded.

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 documents the return object, the dedup constraint, and the follow-up tool, which is substantial for a tool with no output schema. It is slightly incomplete on parameter behavior and error semantics, but for a refresh action with one obvious parameter, it is well covered.

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 explicitly explain how workspaceId behaves, whether it is optional, or what happens when it is omitted. The word 'workspace' in the description loosely implies the parameter's role, but the description does not compensate for the missing schema documentation.

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: 'Trigger an inbox refresh for all LinkedIn accounts in the workspace.' It clearly explains the polling and routing behavior, and the follow-up reference to list_inbox_conversations distinguishes it from reading conversations directly.

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 context for when this tool is relevant and explicitly routes to campaignstack_list_inbox_conversations after the refresh. It does not state when not to use it or compare it with other refresh-related tools, but the context is strong enough for an agent to select it appropriately.

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

A3.6/5.0
Disambiguation3/5

Many tools share the same verb prefix (create_, list_, update_, get_) across closely related resources, so pairs like add_lead_to_external_list vs add_lead_to_sequence, create_signal_agent vs create_signal_watch, and approve_review vs approve_content_post can be confused. The descriptions are unusually detailed and cross-referenced, which mitigates but does not eliminate the ambiguity inherent in a 282-tool surface.

Naming Consistency4/5

Virtually every tool follows the campaignstack_verb_noun snake_case pattern, which is highly predictable. Minor deviations exist: destructive operations mix remove_ and delete_ (remove_lead_list vs delete_campaign), AI generation uses both craft_ and generate_, and the seo_/search_console_ subdomains introduce a second prefix convention.

Tool Count1/5

282 tools is an extreme mismatch by any reasonable standard, exceeding the 50+ threshold by more than 5x. Even for a full B2B outreach platform, this surface is far too large and would be better consolidated into higher-level operations or grouped sub-servers.

Completeness4/5

The tool surface is impressively comprehensive, covering campaigns, workflows, leads, content, ads, SEO, integrations, billing, and more with CRUD-level depth. Minor gaps remain: no single-ICP getter, no direct pause/delete for search watches, and no explicit delete for ad campaigns (only archive via update).

Resources