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by io.github.social-freak-ltd
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Get LinkedIn organization headcount

linkedin_live_organization_headcount_v2
Read-only

Get headcount for a LinkedIn organization. Accepts a organization id. Returns a list (use cursor when paginated).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trimNoIgnored if this tool does not support it.
limitNoIgnored if this tool does not support it.
offsetNoIgnored if this tool does not support it.
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
pageSizeNoIgnored if this tool does not support it.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
maxResultsNoIgnored if this tool does not support it.
max_resultsNoIgnored if this tool does not support it.
organizationIdYesNumeric LinkedIn organization ID.
conversation_idNoEcho the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changed
    • addedInput schema / properties / limit
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "number"
+    },
+    {
+      "type": "boolean"
+    }
+  ],
+  "description": "Ignored if this tool does not support it."
+}
    • addedInput schema / properties / maxResults
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "number"
+    },
+    {
+      "type": "boolean"
+    }
+  ],
+  "description": "Ignored if this tool does not support it."
+}
    • addedInput schema / properties / max_results
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "number"
+    },
+    {
+      "type": "boolean"
+    }
+  ],
+  "description": "Ignored if this tool does not support it."
+}
    • addedInput schema / properties / offset
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "number"
+    },
+    {
+      "type": "boolean"
+    }
+  ],
+  "description": "Ignored if this tool does not support it."
+}
    • changedInput schema / properties / organizationId / description
      Previous value: -"LinkedIn organization ID."New value: +"Numeric LinkedIn organization ID."
    • addedInput schema / properties / pageSize
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "number"
+    },
+    {
+      "type": "boolean"
+    }
+  ],
+  "description": "Ignored if this tool does not support it."
+}
    • addedInput schema / properties / trim
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "number"
+    },
+    {
+      "type": "boolean"
+    }
+  ],
+  "description": "Ignored if this tool does not support it."
+}
  2. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and openWorldHint. The description adds concrete behavioral info beyond that: it returns a list and should use a cursor when paginated. It does not disclose the list's contents, response structure, or auth requirements, but the pagination note is a meaningful addition.

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 description is two sentences with a clear, front-loaded purpose. The phrase 'Accepts a organization id' is mildly redundant with the title and schema, but otherwise the description is compact and free of filler.

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

Completeness2/5

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

With 10 parameters, required context and llm_model, and no output schema, a two-sentence description is insufficient. The cursor concept is mentioned but no parameter is explicitly named, the contents of the returned list are unspecified, and the required auxiliary fields are not highlighted.

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%, so the baseline is 3. The description restates that an organization ID is accepted but adds no parameter semantics beyond the schema. It does not clarify the vague 'Ignored if this tool does not support it' parameters or the required context/llm_model fields.

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 uses a specific verb ('Get') and resource ('headcount for a LinkedIn organization'), and adds that it accepts an organization ID and returns a list. This clearly distinguishes it from sibling tools like linkedin_live_organization_get_v2 or linkedin_live_organization_jobs_v2.

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

Usage Guidelines3/5

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

Usage context is only implied by the purpose: use this tool when you need LinkedIn organization headcount. There are no explicit when-to-use, when-not-to-use, or alternative tool references, so an agent must infer from the name and description alone.

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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