Reach MCP — LinkedIn for AI agents

get_account_request_logs

Read-onlyIdempotent

Get request logs for a LinkedIn account with optional filtering by request_type and pagination using limit/offset. Use to debug a failing account (each LinkedIn call with its status); for totals use get_account_request_logs_stats.

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"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Maximum number of rows to return.
`offset`	No	Number of rows to skip.
`account_id`	Yes	Reach id of the LinkedIn account to act on, from list_accounts.
`request_type`	No	Only rows of this request type (for example send_message, list_conversations, connect).

Output Schema

Name	Required	Description	Default
`result`	No	Request log rows, newest first.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, so the safety profile is covered. The description adds real context beyond that: each row is a single LinkedIn call with its status, and results are paginated via limit/offset. It does not state defaults or caps for limit, which would be the remaining useful detail.

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, no filler, and the core action is front-loaded ahead of the alternative-tool pointer. Every clause 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?

For a read-only, idempotent log-retrieval tool with full parameter coverage, an output schema, and declared annotations, the description supplies everything an agent needs: purpose, filter, pagination, and the totals alternative.

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 schema already documents account_id, request_type, limit, and offset with examples. The description only restates the same filter/pagination concepts already present in the schema, adding no syntax or default values. Baseline 3 applies when the schema does the heavy lifting.

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 (get request logs for a LinkedIn account) plus the scope modifiers it supports. It explicitly distinguishes itself from the sibling get_account_request_logs_stats, which handles totals, so an agent can choose between them 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 a concrete use case (debugging a failing account, seeing each LinkedIn call with its status) and names the alternative tool for totals. The routing condition between this tool and get_account_request_logs_stats is explicit.

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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