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Socials MCP

Socials MCP

by Brainrot-Creations
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

socials_linkedin_connect

Send a LinkedIn connection request by supplying a profile URL. Auto-navigates to the profile, returns connection status, and requires user confirmation before sending.

Instructions

Send a LinkedIn connection request. Pass profile_url (e.g. 'https://linkedin.com/in/username'). Auto-navigates to the profile. Returns status: already_connected, pending_sent, success, etc. IMPORTANT: Always confirm with the user before sending.

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": {
      "profile_url": "<profile_url>"
    },
    "name": "socials_linkedin_connect"
  }
}

Input Schema

NameRequiredDescriptionDefault
noteNoOptional personalized note (max 300 chars)
profile_urlYesLinkedIn profile URL, e.g. 'https://www.linkedin.com/in/satyanadella'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A4.2/5.0
Behavior4/5

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

No annotations are present, so the description carries full burden. It discloses auto-navigation to the profile, return statuses, and the critical requirement to confirm with the user before sending. This is useful behavioral guidance beyond basic tool use.

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 concise (about 40 words) and front-loaded with the main action. The IMPORTANT note is critical and clearly highlighted. No wasted words; every sentence contributes.

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?

For a simple tool with 2 parameters and no output schema, the description provides the core function, an example, return statuses, and a behavioral guideline. It doesn't explain error handling or side effects like network calls, but the essential information for an agent to invoke it 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 the baseline is 3. The description adds a concrete example for profile_url and mentions the return statuses, but does not add significant semantic details for the note parameter beyond what the schema already includes. It doesn't fully compensate for lack of output schema, but parameter-wise it's adequate.

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 'Send a LinkedIn connection request' with a specific action and resource. It distinguishes from sibling tools like socials_linkedin_profile or socials_linkedin_connection_status by focusing on the connection request action.

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 implies when to use this tool (to send a connection request) and provides an example input. It doesn't explicitly name alternatives but the purpose is clear enough that an agent can select it appropriately. It doesn't provide exclusions or alternative tool mentions.

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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