Placed MCP Server

generate_linkedin_profile

Generate a LinkedIn-optimized headline and About section from your resume to enhance professional branding and attract recruiters.

Instructions

Generate LinkedIn-optimized headline and About section based on your resume. Creates compelling professional branding content.

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": {
      "resume_id": "<resume_id>"
    },
    "name": "generate_linkedin_profile"
  }
}

Input Schema

Name	Required	Description	Default
`resume_id`	Yes	Resume ID to base LinkedIn profile on

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0May 8, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations, the description must disclose all behavioral traits. It only reveals that the tool generates content, but does not specify whether it saves to the user's profile, requires special permissions, or has any side effects. The phrase 'creates compelling professional branding content' is vague and lacks operational details.

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 two sentences (17 words) with no redundancy. The first sentence immediately conveys the core action and output, making it efficient and front-loaded.

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

Completeness3/5

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

The description adequately covers the basic purpose but omits details about output format, whether content is truncated, or how to interpret the generated sections. Given the tool's simplicity (one parameter, no output schema), it is minimally sufficient but lacks completeness for an agent to fully anticipate results.

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 coverage is 100% with a single parameter ('resume_id') already described as 'Resume ID to base LinkedIn profile on'. The description adds no further semantics, so it meets the baseline for high coverage without adding value.

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 tool generates a LinkedIn-optimized headline and About section from a resume, with a specific verb ('Generate') and resource ('LinkedIn profile'). It distinguishes itself from sibling tools like 'generate_cover_letter' and 'generate_resume_from_prompt' by focusing on LinkedIn-specific branding content.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. It does not mention prerequisites (e.g., having a resume) or exclusions, leaving the agent to infer usage context from the tool name alone.

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

Deploy Server