Reach MCP — LinkedIn for AI agents

get_account_request_logs_stats

Read-onlyIdempotent

Get aggregated request-log statistics for a LinkedIn account, grouped by day, week, or month, with optional action and date-range filters. Use for the weekly review or to size an account's activity; for individual calls use get_account_request_logs.

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": {
      "account_id": "<account_id>"
    },
    "name": "get_account_request_logs_stats"
  }
}

Input Schema

Name	Required	Description
`end_date`	No	End of the period, ISO 8601 (default: now).
`group_by`	No	Bucket size for the statistics: day, week or month.
`account_id`	Yes	Reach id of the LinkedIn account to act on, from list_accounts.
`start_date`	No	Start of the period, ISO 8601 (default: 30 days ago).
`request_type`	No	Only rows of this request type (for example send_message, list_conversations, connect).

Output Schema

Name	Required	Description	Default
`buckets`	No
`end_date`	No
`group_by`	No
`start_date`	No
`request_type`	No
`available_actions`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safe-read profile is fully covered by structured data. The description adds only that results are bucketed aggregates rather than raw rows, which is modest added context; it does not discuss limits, result caps, or ordering. With annotations carrying the safety burden, a 3 is appropriate.

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?

Two sentences, zero filler; the aggregation scope is front-loaded and the routing to the sibling tool comes second. Every sentence earns its place.

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?

An output schema exists, so return values need not be described, and the schema plus annotations fully cover inputs and safety. The description supplies the one thing structured fields cannot: the aggregation-vs-raw distinction and when to pick this tool.

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 all five parameters (account_id, start/end date, group_by, request_type) are already documented in the schema with defaults and an enum. The description's mention of 'action and date-range filters' and bucket sizes only restates that, adding no syntax or format detail beyond the schema.

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 ('Get aggregated request-log statistics'), names the aggregation dimension (day/week/month) and filters, and explicitly distinguishes itself from the sibling get_account_request_logs. An agent can separate this from the raw-log tool 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 concrete use cases ('weekly review', 'size an account's activity') and names the alternative ('for individual calls use get_account_request_logs') with the condition that selects it. This is the explicit when-to-use/when-not guidance the dimension asks for.

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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