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Salesbot - B2B outreach LinkedIn MCP Server with CRM and email

by cz.salesbot
OverviewDiscussionsAdmin

get_chat_messages

Read-onlyIdempotent

Read the messages of a single LinkedIn conversation in real time (newest last). Use the chat_id from list_inbox_chats. is_sender=true marks messages sent by the account owner (you). Use this to understand context before composing a reply with reply_to_chat. Inbox operations are capped at 3 MCP calls per minute; request only the needed thread/page.

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": {
      "chat_id": "<chat_id>"
    },
    "name": "get_chat_messages"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax messages (1-50, default 15)
cursorNoPagination cursor. Optional.
chat_idYesChat ID from list_inbox_chats
profile_idNoUUID of the user_profile. Optional — defaults to active profile.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), but the description adds genuinely new behavior: a real-time read with newest-last ordering, the meaning of is_sender=true, and a hard rate limit of 3 inbox MCP calls per minute. The rate limit and ordering semantics are exactly the kind of context annotations cannot convey.

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?

Four compact sentences, front-loaded with the core action and scope, then routing, then a usage constraint. No filler; each clause carries actionable information.

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 thread fetch with no output schema, the description covers purpose, data source, ordering, field semantics, downstream use, and rate limits. Nothing an agent needs to invoke it correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds value beyond the schema by specifying where chat_id comes from (list_inbox_chats) and clarifying the is_sender output field. It does not document limit/cursor/profile_id further, which the schema already handles.

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 (Read) and resource (messages of a single LinkedIn conversation) with scope qualifiers (real time, newest last). It is clearly distinguishable from list_inbox_chats (chats, not messages) and reply_to_chat (writes), so an agent can select it without opening a sibling 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 routes the agent: take chat_id from list_inbox_chats and use this to gather context before composing a reply with reply_to_chat. It also constrains call behavior with 'request only the needed thread/page', giving both when-to-use and how-to-use guidance.

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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