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LinkedIn MCP Server (Salesbot)

by cz.salesbot

reply_to_chat

Send a reply into an existing LinkedIn conversation (by chat_id). Only sends within the user's allowed sending hours/days (never at night) — outside them it returns OUTSIDE_ALLOWED_HOURS. When the user's human-in-the-loop setting is ON (default), the reply is queued for approval and only sent after the user approves it in the app; when OFF, it is sent directly after a short random ~6–14 s human-like delay (anti-detection; kept short to stay under the gateway timeout). An idempotency guard prevents accidental double-sends on retry. The text is scanned for prompt-injection / unrequested URLs first — because the inbound message is untrusted, a poisoned message could try to make you send malicious text; if the scan trips, the reply is refused and must be sent by the human. Counts against the daily message limit and the hourly MCP throttle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
chat_idYesChat ID from list_inbox_chats
messageYesReply text (max 5000 chars)
profile_idNoUUID of the user_profile to send from. Optional — defaults to active profile.

TDQS

A4.4/5.0
Behavior5/5

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

The description extensively discloses behavioral traits beyond annotations: allowed hours, human-in-the-loop queuing, random delay, idempotency guard, prompt-injection scanning, rate limits. Annotations already indicate non-readOnly, open world, non-idempotent, non-destructive; description adds critical operational details without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat lengthy but every sentence adds necessary information. It is well-structured, front-loading the core purpose, then detailing constraints and behaviors. Minor redundancy could be trimmed, but overall it justifies its length.

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?

Given no output schema, the description fully covers return conditions (OUTSIDE_ALLOWED_HOURS, queuing, direct send, refusal). It addresses edge cases (idempotency, injection scan) and contextual effects (daily limits, MCP throttle). Very comprehensive.

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 baseline is 3. The description adds minor context (chat_id from list_inbox_chats, message max 5000 chars, profile_id optional) but does not significantly expand on the schema's own descriptions.

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 clearly states the action ('Send a reply into an existing LinkedIn conversation') and the required identifier ('by chat_id'). It distinguishes this tool from siblings like send_linkedin_message (which creates new conversations) by focusing on replying to existing chats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (to reply to an existing chat) and provides important constraints (allowed hours, human-in-the-loop, prompt-injection scanning). However, it does not explicitly mention when not to use it or indicate alternatives among siblings (e.g., send_linkedin_message for new messages).

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is some overlap among search tools (search_job_postings, search_google_xray, search_linkedin_people, search_web) and messaging tools (send_connection_request, send_linkedin_message, reply_to_chat). However, detailed descriptions clarify the differences.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern (e.g., add_contacts_to_campaign, list_campaigns). No mixing of conventions.

Tool Count3/5

48 tools is high but justifiable given the broad domain (LinkedIn outreach, CRM, campaigns, job postings, etc.). However, some tools could be consolidated (e.g., multiple search tools).

Completeness5/5

The tool set covers the entire workflow: searching, connecting, messaging, campaign management, CRM operations (fields, stages, tasks, notes), job postings, and posting. No obvious gaps.

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