Summary
Connects Claude to the StoryLenses API for programmatic cover letter generation. Exposes five tools: analyze job postings to extract structured fields like requirements and culture signals, match candidate CVs against those fields, generate narrative-driven letters using selectable archetypes, run quality checks with scoring, and list available writing styles. The workflow is sequential: parse a job URL, feed the analysis plus a resume into the matcher, then generate a letter based on the match data and chosen narrative tone. Requires an API key from storylenses.app with tiered limits starting at 10 free generations monthly. Useful if you're building job application automation or want structured job data extraction beyond basic scraping.
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Public tool metadata for what this MCP can expose to an agent.
5 tools
storylenses_analyze_jobExtract 15+ structured fields from a job posting — role requirements, company challenges, culture signals, recruiter priorities3 params
Extract 15+ structured fields from a job posting — role requirements, company challenges, culture signals, recruiter priorities
Parameters* required
job_textstringRaw text of the job posting (use if no URL)
job_urlstringURL of the job posting to analyze
localestringResponse language (en, de, es, or pt)one of
en · de · es · ptdefault: enstorylenses_match_profileMatch a candidate profile/CV against job data — identifies fit score, matching skills, career gaps, and strongest narrative angle3 params
Match a candidate profile/CV against job data — identifies fit score, matching skills, career gaps, and strongest narrative angle
Parameters* required
candidate_cvstringCandidate's CV or resume as plain text
job_analysisobjectJob analysis output from storylenses_analyze_job
localestringResponse language (en, de, es, or pt)one of
en · de · es · ptdefault: enstorylenses_generate_letterGenerate a story-driven cover letter using matched data and a narrative archetype. Supports en/de/es/pt.7 params
Generate a story-driven cover letter using matched data and a narrative archetype. Supports en/de/es/pt.
Parameters* required
archetypestringNarrative archetype ID (use storylenses_list_archetypes to see options)default: golden-fleece
candidate_namestringCandidate's full name for the letter greeting
job_analysisobjectJob analysis from storylenses_analyze_job
lengthstringLetter length — short (150-200 words), medium (250-350), or full (400-500)one of
short · medium · fulldefault: mediumlocalestringOutput language (en, de, es, or pt)one of
en · de · es · ptdefault: enmatch_dataobjectMatch data from storylenses_match_profile
tonestringWriting tone — professional, conversational, confident, etc.one of
professional · conversational · formal · confident · analytical · storytellingdefault: professionalstorylenses_quality_checkScore and evaluate a cover letter for relevance, narrative strength, and completeness. Returns score 0-100 with actionable feedback.3 params
Score and evaluate a cover letter for relevance, narrative strength, and completeness. Returns score 0-100 with actionable feedback.
Parameters* required
job_analysisobjectJob analysis from storylenses_analyze_job for context
letter_textstringThe cover letter text to evaluate (minimum 200 characters)
localestringFeedback language (en, de, es, or pt)one of
en · de · es · ptdefault: enstorylenses_list_archetypesReturn available narrative archetypes with descriptions so the agent or user can select a style1 params
Return available narrative archetypes with descriptions so the agent or user can select a style
Parameters* required
localestringDescription language (en, de, es, or pt)one of
en · de · es · ptdefault: enStoryLenses MCP Server
AI-powered cover letter generation for MCP-compatible agents. The first production MCP server for job applications.
Tools
| Tool | Description |
|---|---|
storylenses_analyze_job | Extract 15+ structured fields from a job posting |
storylenses_match_profile | Match a candidate CV against job data |
storylenses_generate_letter | Generate a story-driven cover letter |
storylenses_quality_check | Score and evaluate a cover letter |
storylenses_list_archetypes | List available narrative archetypes and tones |
Setup
Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"storylenses": {
"command": "npx",
"args": ["-y", "@storylenses/mcp-server"],
"env": {
"STORYLENSES_API_KEY": "your-api-key"
}
}
}
}
Cursor / VS Code
Add to .cursor/mcp.json or VS Code MCP settings:
{
"mcp": {
"servers": {
"storylenses": {
"command": "npx",
"args": ["-y", "@storylenses/mcp-server"],
"env": {
"STORYLENSES_API_KEY": "your-api-key"
}
}
}
}
}
Docker
docker run -e STORYLENSES_API_KEY=your-key ghcr.io/benediktgirz/storylenses-mcp-server
Environment Variables
| Variable | Required | Description |
|---|---|---|
STORYLENSES_API_KEY | Yes | Your API key from storylenses.app/mcp |
STORYLENSES_API_URL | No | API base URL (default: https://www.storylenses.app) |
Example Workflow
// 1. Analyze job posting
const job = await callTool("storylenses_analyze_job", {
job_url: "https://linkedin.com/jobs/view/12345"
});
// 2. Match candidate profile
const match = await callTool("storylenses_match_profile", {
job_analysis: job,
candidate_cv: "Senior engineer with 7 years React experience..."
});
// 3. Generate cover letter
const letter = await callTool("storylenses_generate_letter", {
job_analysis: job,
match_data: match,
candidate_name: "Alex Chen",
archetype: match.suggestedArchetype
});
// 4. Quality check
const score = await callTool("storylenses_quality_check", {
letter_text: letter.letter_text,
job_analysis: job
});
Testing
npm install
STORYLENSES_API_KEY=your-key npm test
Get an API Key
Visit storylenses.app/mcp to get your API key.
- Free: 10 generations/month
- Developer ($29/mo): 200 generations
- Scale ($99/mo): 1,000 generations
License
MIT
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Package@storylenses/mcp-server
TransportSTDIO, HTTP
AuthRequired
UpdatedApr 7, 2026