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campaignstack_list_inbox_conversations

Read-onlyIdempotent

List inbox conversations across all LinkedIn accounts in the workspace. Returns up to 50 conversations sorted by most recent activity, with participant details, last message preview, read/unread status, and associated lead info. Each conversation includes a conversationRef { platform, id } used as the identifier for other inbox tools. Use unreadOnly: true to filter to unread threads only. Use identityFilter to restrict to specific LinkedIn account IDs (use campaignstack_list_accounts to find them). Use campaignstack_get_conversation to fetch full message history for a thread.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unreadOnlyNo
workspaceIdNo
identityFilterNo

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark this as read-only and idempotent, so no safety contradiction exists. The description adds meaningful behavioral context: a 50-conversation cap, sort order by most recent activity, returned fields, and the conversationRef structure used by other inbox tools. It also discloses that the tool spans all LinkedIn accounts unless filtered.

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?

Four sentences, all dense and purposeful. The core action and scope appear first, followed by return details, identifier semantics, and filter/alternative guidance. No wasted words or redundant restatements of the schema.

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 no output schema, the description thoroughly covers return shape, limits, sorting, filters, and related tools. The only notable gap is the unexplained workspaceId parameter, which prevents full contextual completeness for agents needing to target a specific workspace.

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?

With 0% schema description coverage, the description must compensate for parameter meaning. It explains unreadOnly and identityFilter well, even pointing to campaignstack_list_accounts for identity values. However, workspaceId is never described, leaving one of three parameters undocumented and forcing the agent to infer its purpose.

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: 'List inbox conversations across all LinkedIn accounts in the workspace.' It clearly distinguishes this from siblings like campaignstack_get_conversation and campaignstack_get_inbox_unread_count by focusing on listing conversation summaries rather than fetching message history or counts.

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

Usage Guidelines5/5

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

The description gives explicit usage guidance: use unreadOnly to filter unread threads, use identityFilter to restrict by LinkedIn account, and use campaignstack_get_conversation for full message history. This tells the agent when to use this tool versus a clear alternative.

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).

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