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Check Linkedin Daily Usage

check_linkedin_daily_usage
Read-only

Call this before sending multiple LinkedIn messages, InMails, or connection requests to get a fresh read of today's headroom (limits, remaining, ramp-up) plus the current queue counts — re-call it after queueing or approving sends, which leave the in-context figures out of date. For the per-contact queue (names, scheduled times, messages), use manage_linkedin_invite_queue with action='status'. Dict with usage counts, limits, queue counts, and configurable_limits — per action the standard default, the tier-aware ceiling (the max the user can raise it to), and the user's current custom cap (None = using the default). Quote ceiling when the user asks how high they can set a cap.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
on_behalf_ofNoAn agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / on_behalf_of
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "An agency client's email, from list_agency_clients, to run this call in that client's account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."
+}
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish readOnlyHint=true, so the safety profile is covered. The description adds a genuinely useful behavioral trait beyond that: the figures are a 'fresh read' whose values go stale after queueing/approving sends, which is exactly the operational nuance an agent needs. It does not discuss error behavior or any throttling on the call itself, so it stops short of a 5.

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

Conciseness4/5

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

The summary block is front-loaded with the tool's purpose and the usage trigger, and the tagged structure separates summary from returns cleanly. Minor redundancy between the title, the first sentence, and the returns block, but every sentence still carries information.

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

Completeness5/5

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

There is no output schema, so the `<returns>` block is doing necessary work – it enumerates usage counts, limits, queue counts, and the `configurable_limits` structure (default/ceiling/custom) and even tells the agent to quote `ceiling` for cap questions. Combined with the when-to-call guidance, an agent has everything needed to invoke and interpret the tool correctly.

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?

Schema description coverage is 100% – the single `on_behalf_of` parameter is fully documented in the schema (agency client email, cross-account execution, omit for own account). The description adds nothing about it, which is acceptable since the schema does the heavy lifting. Baseline 3 applies.

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?

States a specific verb+resource: 'Check LinkedIn usage limits and invite queue counts.' It also explicitly distinguishes itself from the nearest sibling by naming `manage_linkedin_invite_queue` with action='status' for the per-contact queue. An agent can tell the two apart without opening either schema.

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?

Gives explicit trigger conditions ('Call this before sending multiple LinkedIn messages, InMails, or connection requests'), a re-invocation rule ('re-call it after queueing or approving sends, which leave the in-context figures out of date'), and names the alternative tool for a related-but-different need. Nothing is left to inference.

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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