Sliq

Set LinkedIn daily limits

set_linkedin_daily_limits

limits is a partial {action: cap} map — only the actions you name change, the rest keep their current cap. Each cap is clamped to that action's tier-aware ceiling (the ceiling in check_linkedin_daily_usage's configurable_limits); a cap of 0 or below resets that action to its default. Action keys are the configurable_limits keys (connection_request, message, inmail, …); an unknown key is rejected with the full valid list.

The number the user sets is the day-to-day center, not a fixed count: like Sliq's defaults, each cap carries a small ±jitter so sending doesn't read as automation, so the actual daily number varies by a few around the set value (never above the tier ceiling). Tell the user the value you set, not that they'll send exactly that many.

Returns {success, custom_daily_limits}, the resulting stored cap map. Dict with success and the stored custom_daily_limits map.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "limits": "<limits>"
    },
    "name": "set_linkedin_daily_limits"
  }
}

Input Schema

Name	Required	Description	Default
`limits`	Yes	Partial map of action -> daily cap. 0 or below resets that action to default.
`on_behalf_of`	No	A teammate's email, or an agency client's from list_agency_clients, to run this call in that account: their agents, settings, connected accounts, and credits. Omit it to act on your own account.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 6, 2026

changedInput schema / properties / on_behalf_of / description

Previous value: -"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."New value: +"A teammate's email, or an agency client's from list_agency_clients, to run this call in that account: their agents, settings, connected accounts, and credits. Omit it to act on your own account."

Changed1 schema field changedOct 19, 2026

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."
+}

First observedSep 15, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Well beyond the annotations, which only say readOnlyHint=false and destructiveHint=false. The description discloses the partial-merge semantics, tier-ceiling clamping, the 0-or-below reset behavior, rejection of unknown keys with the valid list surfaced, and the ±jitter behavior that means the stored cap is a center rather than an exact count — plus the instruction to phrase results to the user accordingly. That jitter note is precisely the kind of non-obvious behavior an agent would otherwise get wrong in its reply.

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?

Front-loaded with the purpose and trigger, then parameter semantics, then return shape — a sensible order with little waste. It is on the long side and the raw summary/returns XML tags leak into the prose, which costs a bit of polish, but every sentence carries information an agent needs to call it correctly.

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?

No output schema exists, yet the description still names the return shape ("Returns {success, custom_daily_limits}"), so nothing needed to interpret the response is missing. For a mutating tool with a nested partial-map parameter and no destructive hint, the clamping, reset, and jitter disclosures close the gaps that would otherwise cause incorrect calls or incorrect user-facing claims.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: what a partial map means for untouched actions, how caps are clamped to the ceiling from configurable_limits, that 0 or below is a reset rather than a literal zero, and that action keys must come from the configurable_limits key set. The one gap is that on_behalf_of is not addressed in the description at all, though its schema text is self-sufficient.

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 and resource: "Change the user's per-action LinkedIn daily caps", and pins down the domain by reference to the AI Preferences settings card. It also names the read-side sibling (check_linkedin_daily_usage), so the agent can distinguish the write from the read 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 an explicit trigger ("Call this when the user asks to raise or lower how many connection requests, messages, InMails, etc. they send per day"), an explicit behavioral directive ("actually make the change, don't just describe the UI"), and an explicit follow-up ("Re-read check_linkedin_daily_usage afterward to confirm"). When-to-use, what-not-to-do, and the verification alternative are all spelled out.

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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