Reach MCP — LinkedIn for AI agents

list_scheduled_posts

Read-onlyIdempotent

List scheduled LinkedIn posts for an account, paged. Returns post URNs, text content, and scheduled publish times. Use to see what is queued to publish; published posts are not listed here, use list_user_posts for those.

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": "list_scheduled_posts"
  }
}

Input Schema

Name	Required	Description
`count`	No	Page size.
`start`	No	Pagination offset, 0-based.
`account_id`	Yes	Reach id of the LinkedIn account to act on, from list_accounts.

Output Schema

Name	Required	Description
`count`	No
`posts`	No
`start`	No
`total`	No	Total number of scheduled posts.

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 cover the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint). The description adds useful behavioral context beyond that: it discloses the paged nature and the returned fields (URNs, text, scheduled publish times), and the scoping fact that only queued posts appear. It does not mention auth needs or rate limits, so not a full 5.

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?

Three tight sentences, front-loaded with the purpose, followed by return contents and then the routing to the alternative tool. No filler.

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?

Output schema exists, so return values need not be restated, yet the description still summarizes them. With annotations carrying safety and the schema carrying parameters, everything an agent needs to call this correctly is present.

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 count, start, and account_id are already documented in the schema. The description only adds 'paged' generically without elaborating on count/start semantics, so it does not meaningfully exceed the schema baseline.

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 (List) and resource (scheduled LinkedIn posts for an account) with scope, and explicitly distinguishes itself from the sibling list_user_posts. An agent can separate it from list_user_posts 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?

Explicitly states when to use it ('to see what is queued to publish'), when not (published posts are not listed here), and names the alternative (list_user_posts) for the excluded case. 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.

All Reach MCP — LinkedIn for AI agents tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?