Skip to main content
Glama

inbox

Read-only

Read LinkedIn conversations and drafted replies. Use list, read, or comment_drafts actions to manage messages and drafts.

Instructions

Read any conversation in your LinkedIn inbox, and the drafted replies.

Replying, approving a draft and discarding one are answer_inbox.

Args:
    action: "list", "read" or "comment_drafts".
    chat_id: Which conversation.
    name: Find the conversation by the person's name.
    limit: How many to show.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
limitNo
actionNolist
chat_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.10.389
    • removedInput schema / properties / text
      Removed value: -{
      -  "default": "",
      -  "title": "Text",
      -  "type": "string"
      -}
  2. First observedv0.10.375

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description reinforces the read-only behavior by mentioning that drafted replies can be read and that write actions belong to answer_inbox. It adds useful behavioral context without contradicting the annotations, and no hidden side effects need to be disclosed for a read operation.

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?

The description is compact and well-structured: a direct purpose statement, a brief sibling-routing note, and a minimal argument list. Every sentence earns its place and there is no redundant filler.

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

Completeness4/5

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

All parameters are semantically documented, the read/write boundary is explicit, and the presence of an output schema means return values need not be described in prose. The only minor gap is the lack of detail on how chat_id and name interact or take precedence, but this does not prevent correct usage.

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

Parameters5/5

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

The input schema has 0% description coverage and no enums, so the description carries the full burden of explaining parameters. It defines all four: action with allowed values 'list', 'read', and 'comment_drafts'; chat_id as the conversation identifier; name for finding the conversation by person; and limit for display count. This fully compensates for the empty 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?

The description opens with a clear verb and resource: 'Read any conversation in your LinkedIn inbox, and the drafted replies.' It also explicitly distinguishes itself from answer_inbox, which handles replying, approving, and discarding drafts, so an agent can easily tell this tool apart from its closest sibling.

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?

The description explicitly states which actions belong to answer_inbox instead of this tool: 'Replying, approving a draft and discarding one are answer_inbox.' This gives a clear when-not-to-use condition and names the alternative, providing strong routing guidance without leaving the decision to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.