Model Context Protocol

The Cover Letter Engine for AI Agents

Your agent finds the jobs. StoryLenses writes the letters that get interviews. Add narrative-driven cover letter generation to any MCP-compatible agent in minutes.

Get Started

5 Composable Tools

A complete cover letter workflow for your agent — from job analysis to quality assurance.

storylenses_analyze_job

Extract 15+ structured fields from a job posting — role requirements, company challenges, culture signals, recruiter priorities.

storylenses_match_profile

Match a candidate profile against job data — identifies fit score, matching skills, career gaps, and the strongest narrative angle.

storylenses_generate_letter

Generate a story-driven cover letter using matched data and a selected narrative archetype. Supports 4 languages.

storylenses_quality_check

Score and evaluate a cover letter for relevance, narrative strength, and completeness. Returns actionable feedback.

storylenses_list_archetypes

Return available narrative archetypes with descriptions so the agent or user can select a style.

Why StoryLenses, Not a Raw LLM Call?

Your agent can send a CV and job description to GPT with a prompt. The result reads like AI slop.

StoryLenses uses narrative science — 15+ structural archetypes, deep job posting analysis, and profile-to-role matching — to produce letters that read like a human strategist wrote them. The difference is not in the writing quality. It is in the thinking that happens before the writing starts.

Raw LLM Call

✗ Generic 3-paragraph structure
✗ No job posting analysis
✗ No profile matching
✗ Same output every time
✗ Sounds like a chatbot

StoryLenses MCP

✓ 15+ narrative archetypes
✓ 15+ structured job fields extracted
✓ CV-to-role matching with gap analysis
✓ Unique output per job/candidate pair
✓ Reads like a human strategist

Quickstart

Connect in under 2 minutes.

Claude Desktop

{
  "mcpServers": {
    "storylenses": {
      "url": "https://mcp.storylenses.app/sse",
      "headers": {
        "Authorization": "Bearer YOUR_API_KEY"
      }
    }
  }
}

Cursor / VS Code

{
  "mcp": {
    "servers": {
      "storylenses": {
        "url": "https://mcp.storylenses.app/sse",
        "headers": {
          "Authorization": "Bearer YOUR_API_KEY"
        }
      }
    }
  }
}

TypeScript SDK

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { SSEClientTransport } from "@modelcontextprotocol/sdk/client/sse.js";

const transport = new SSEClientTransport(
  new URL("https://mcp.storylenses.app/sse"),
  { requestInit: { headers: { Authorization: "Bearer YOUR_API_KEY" } } }
);
const client = new Client({ name: "my-agent", version: "1.0" });
await client.connect(transport);

Example Workflow

// 1. Analyze the job posting
const job = await client.callTool("storylenses_analyze_job", {
  job_url: "https://linkedin.com/jobs/view/12345"
});

// 2. Match candidate profile
const match = await client.callTool("storylenses_match_profile", {
  job_analysis_id: job.id,
  candidate_profile: { cv_text: resumeText }
});

// 3. Generate cover letter
const letter = await client.callTool("storylenses_generate_letter", {
  job_analysis_id: job.id,
  match_id: match.id,
  archetype: match.suggested_archetype,
  locale: "en"
});

// 4. Quality check
const score = await client.callTool("storylenses_quality_check", {
  letter_text: letter.letter_text,
  job_analysis_id: job.id
});

console.log(letter.letter_text);  // Story-driven cover letter
console.log(score.score);          // 87/100