What can valjs-mcp-beta do?

json resume schema validation and transformation, resume field extraction and structured parsing, resume content enrichment and enhancement, multi-format resume export and generation, resume metadata and analytics extraction, resume validation with custom rule sets

valjs-mcp-beta

MCP ServerFree

ModelContextProtocol server for enhancing JSON Resumes

Open Source

/ 100

6 capabilities

Capabilities6 decomposed

json resume schema validation and transformation

Medium confidence

Validates JSON Resume documents against the official JSON Resume schema specification and transforms resume data into normalized, schema-compliant structures. Uses schema-based validation to detect structural errors, missing required fields, and type mismatches before downstream processing. Implements transformation pipelines that map user-provided resume formats into canonical JSON Resume format with field mapping and data coercion.

Solves for

Validate that a resume JSON file conforms to the JSON Resume standard before exporting or sharingTransform resume data from custom or legacy formats into standardized JSON Resume structureDetect and report schema violations with specific field paths and validation error messagesEnsure resume data integrity across different tools and platforms that consume JSON Resume

Best for

Resume management platforms and career tools integrating JSON Resume standard

Teams building resume parsing or enrichment pipelines

Developers creating resume export/import functionality across multiple formats

Requires

Node.js 16+

MCP client compatible with ModelContextProtocol specification

Valid JSON Resume schema definition accessible at runtime

Limitations

Limited to JSON Resume schema v1.x — custom extensions beyond spec are not validated

No support for legacy resume formats (DOCX, PDF) — requires pre-conversion to JSON

Validation is structural only — does not perform semantic checks (e.g., date consistency, skill relevance)

What makes it unique

Implements JSON Resume validation as an MCP server, enabling any MCP-compatible client (Claude, custom agents, IDEs) to validate and transform resumes without direct library dependencies — validation logic is exposed as remote procedures rather than embedded in client code

vs alternatives

Decouples resume validation from client applications via MCP protocol, allowing centralized schema updates and validation logic without requiring client-side library updates

resume field extraction and structured parsing

Medium confidence

Extracts and parses individual resume fields (contact info, work history, education, skills, etc.) from unstructured or semi-structured resume text using pattern matching and field-specific parsers. Decomposes resume content into discrete, typed fields with support for multiple date formats, phone number variations, and skill list parsing. Returns structured objects with normalized field values and confidence metadata.

Solves for

Parse resume text to extract contact information, employment history, and education detailsConvert freeform resume text into structured JSON Resume format fieldsNormalize dates, phone numbers, and other common resume fields into standard formatsExtract and categorize skills from resume text with optional deduplication

Best for

Resume parsing and ATS (Applicant Tracking System) integration

Resume enrichment pipelines that need to normalize and structure raw resume data

Developers building resume import tools that accept text or markdown input

Requires

Node.js 16+

MCP client with text input capability

Resume content as plain text or markdown string

Limitations

Accuracy depends on resume formatting consistency — highly unstructured or non-standard layouts may have lower extraction accuracy

No OCR support — requires text input, not image or PDF scans

Field extraction is rule-based and may miss domain-specific or non-standard field types

What makes it unique

Exposes resume parsing as MCP tools, enabling LLM agents and Claude to directly extract and structure resume fields without requiring separate NLP libraries or API calls — parsing logic runs server-side with MCP protocol as the integration layer

vs alternatives

Tighter integration with LLM workflows compared to standalone parsing libraries; agents can iteratively refine extraction by calling tools multiple times with different input variations

resume content enrichment and enhancement

Medium confidence

Enhances resume content by generating improved descriptions, expanding abbreviated fields, and adding missing context to make resumes more compelling. Uses template-based generation and contextual expansion to improve job descriptions, skill descriptions, and achievement statements. Integrates with LLM capabilities to suggest improvements while maintaining factual accuracy and user intent.

Solves for

Generate more impactful job descriptions from brief resume entriesExpand abbreviated skill names and add context to skill listingsSuggest improvements to resume language and phrasing for better ATS and recruiter visibilityFill in missing or incomplete resume sections with contextually appropriate content

Best for

Resume optimization tools and career coaching platforms

Job seekers using Claude or LLM agents to improve their resume quality

Resume-as-a-service platforms that need to enhance user-submitted content

Requires

Node.js 16+

MCP client with LLM capabilities (Claude or compatible model)

Existing resume data in JSON Resume format or as parsed fields

Limitations

Enrichment is generative and may introduce inaccuracies if not reviewed — requires human validation

No access to external job market data — suggestions are based on general best practices, not role-specific requirements

Cannot verify factual claims in resume content — enhancement is stylistic and structural only

What makes it unique

Implements resume enrichment as MCP tools that integrate with Claude's native capabilities, allowing Claude to suggest and apply improvements directly within conversation context without requiring separate API calls or external services

vs alternatives

Enables in-context resume improvement within Claude conversations, providing real-time suggestions and edits without context switching to external tools or platforms

multi-format resume export and generation

Medium confidence

Generates resume output in multiple formats (JSON, YAML, HTML, Markdown, PDF) from a canonical JSON Resume data structure. Implements format-specific templates and serializers that handle layout, styling, and format-specific constraints. Supports customizable templates and theme selection for HTML/PDF output while maintaining data consistency across all formats.

Solves for

Export a resume from JSON Resume format to HTML for web viewing or sharingGenerate a Markdown version of a resume for GitHub profiles or portfolio sitesConvert resume data to YAML for configuration management or version controlCreate PDF output with custom styling and layout for traditional job applications

Best for

Resume management platforms that need to support multiple output formats

Developers building resume builders or portfolio tools

Teams managing resume templates and standardized export formats

Requires

Node.js 16+

MCP client with file output capability

Valid JSON Resume data structure as input

Limitations

PDF generation requires additional dependencies (e.g., puppeteer, wkhtmltopdf) — may add latency and system requirements

Template customization is limited to predefined themes — complex custom layouts require template modification

No support for advanced PDF features (forms, interactivity) — output is static document format

What makes it unique

Provides multi-format export as MCP tools, allowing Claude and other agents to generate resume outputs in any supported format directly within conversation — no separate export step or tool switching required

vs alternatives

Integrated export within MCP protocol enables agents to generate and iterate on resume formats without external tool dependencies; format conversion happens server-side with results returned to client

resume metadata and analytics extraction

Medium confidence

Analyzes resume content to extract metadata and compute analytics such as total years of experience, skill frequency, education level, and employment gaps. Implements calculation logic for derived fields (e.g., years between dates, skill count) and generates summary statistics about resume composition. Provides insights into resume structure completeness and content distribution.

Solves for

Calculate total years of professional experience from work history datesIdentify and flag employment gaps or unusual career transitionsGenerate skill frequency analysis to identify most-emphasized competenciesAssess resume completeness and suggest missing sections or fields

Best for

Resume analysis and ATS optimization tools

Career coaching platforms that provide resume feedback

Job matching algorithms that need resume profile summaries

Requires

Node.js 16+

MCP client

Valid JSON Resume data with populated date fields

Limitations

Gap detection is date-based only — cannot distinguish intentional breaks (sabbatical, education) from unemployment

Skill analysis is frequency-based — does not assess skill relevance or market demand

Completeness scoring is based on field presence, not content quality or depth

What makes it unique

Computes resume analytics server-side via MCP, allowing agents to analyze resume profiles and make data-driven decisions (e.g., suggest experience-level appropriate roles) without client-side calculation logic

vs alternatives

Centralized analytics computation via MCP enables consistent analysis across all clients and allows agents to reason about resume profiles with derived metrics unavailable in raw resume data

resume validation with custom rule sets

Medium confidence

Validates resume content against configurable rule sets beyond schema validation, including custom business rules, content policies, and quality standards. Supports rule definitions for field length constraints, required field combinations, content restrictions, and custom validation logic. Returns detailed validation reports with rule violations and remediation suggestions.

Solves for

Enforce company-specific resume requirements (e.g., minimum years of experience, required certifications)Validate resume content against content policies (e.g., no personal pronouns, specific formatting requirements)Check for completeness based on job level or role requirementsGenerate validation reports with specific violations and suggested fixes

Best for

Enterprise ATS systems with custom resume validation requirements

Recruitment platforms with standardized resume quality standards

Teams managing resume templates with specific content guidelines

Requires

Node.js 16+

MCP client

Rule set configuration (JSON or code-based)

Limitations

Rule configuration requires upfront definition — no automatic rule inference from examples

Custom validation logic is limited to predefined rule types — complex business logic requires code changes

No machine learning-based validation — rules are deterministic and require manual updates as standards evolve

What makes it unique

Implements configurable validation rules as MCP tools, enabling clients to define and enforce custom resume standards without modifying server code — rule sets are passed as parameters to validation tools

vs alternatives

Decouples validation rules from server implementation, allowing dynamic rule updates and client-specific validation policies without redeploying the MCP server

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with valjs-mcp-beta, ranked by overlap. Discovered automatically through the match graph.

MCP Server24

@jsonresume/mcp

ModelContextProtocol server for enhancing JSON Resumes

json resume schema validation and transformationresume field extraction and normalizationresume versioning and variant management

3 shared capabilities

MCP Server25

@mcpflow.io/mcp

ModelContextProtocol server for enhancing JSON Resumes

json resume schema validation and transformation via mcpschema-aware resume data validation and error reportingresume data normalization and format conversion

3 shared capabilities

MCP Server23

@jsonresume/jsonresume-mcp

ModelContextProtocol starter server

resume parsing and data extractionresume validation against json resume schema

2 shared capabilities

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resume-parsing-and-structured-extraction

1 shared capability

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Applaime

Revolutionize job applications with AI: ATS optimization, personalized materials, interview...

resume parsing and structured profile extraction

1 shared capability

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CoverQuick

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resume-content-extraction-and-parsing

1 shared capability

Best For

✓Resume management platforms and career tools integrating JSON Resume standard
✓Teams building resume parsing or enrichment pipelines
✓Developers creating resume export/import functionality across multiple formats
✓Resume parsing and ATS (Applicant Tracking System) integration
✓Resume enrichment pipelines that need to normalize and structure raw resume data
✓Developers building resume import tools that accept text or markdown input
✓Resume optimization tools and career coaching platforms
✓Job seekers using Claude or LLM agents to improve their resume quality

Known Limitations

⚠Limited to JSON Resume schema v1.x — custom extensions beyond spec are not validated
⚠No support for legacy resume formats (DOCX, PDF) — requires pre-conversion to JSON
⚠Validation is structural only — does not perform semantic checks (e.g., date consistency, skill relevance)
⚠Accuracy depends on resume formatting consistency — highly unstructured or non-standard layouts may have lower extraction accuracy
⚠No OCR support — requires text input, not image or PDF scans
⚠Field extraction is rule-based and may miss domain-specific or non-standard field types

Requirements

Node.js 16+MCP client compatible with ModelContextProtocol specificationValid JSON Resume schema definition accessible at runtimeMCP client with text input capabilityResume content as plain text or markdown stringMCP client with LLM capabilities (Claude or compatible model)Existing resume data in JSON Resume format or as parsed fieldsMCP client with file output capability

Input / Output

Accepts: JSON object (resume data), JSON string (serialized resume), plain text (resume content), markdown (formatted resume), string (individual resume field or section), JSON object (resume data in JSON Resume format), rule set configuration (JSON object or array)

Produces: JSON object (validated/transformed resume), validation error array with field paths and messages, JSON object with typed fields (name, email, phone, workHistory[], education[], skills[]), extraction metadata (confidence scores, parsing warnings), JSON object (enhanced resume with improved descriptions), string (enhanced field value with suggestions), JSON string (JSON format), YAML string (YAML format), HTML string (HTML format with inline styles), Markdown string (Markdown format), PDF binary (PDF document), JSON object (analytics summary with computed fields), metadata object (experience years, gap count, skill frequency map), validation report object (violations array with rule names and messages), remediation suggestions (array of suggested fixes)

UnfragileRank

Adoption15%(25% weight)

Quality14%(25% weight)

Ecosystem30%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

6 capabilities

Visit valjs-mcp-beta→

Package Details

npm

Registry

2.1.1

Version

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ModelContextProtocol server for enhancing JSON Resumes

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Capabilities6 decomposed

json resume schema validation and transformation

Medium confidence

Solves for

Best for

Resume management platforms and career tools integrating JSON Resume standard

Teams building resume parsing or enrichment pipelines

Developers creating resume export/import functionality across multiple formats

Requires

Node.js 16+

MCP client compatible with ModelContextProtocol specification

Valid JSON Resume schema definition accessible at runtime

Limitations

Limited to JSON Resume schema v1.x — custom extensions beyond spec are not validated

No support for legacy resume formats (DOCX, PDF) — requires pre-conversion to JSON

Validation is structural only — does not perform semantic checks (e.g., date consistency, skill relevance)

What makes it unique

vs alternatives

Decouples resume validation from client applications via MCP protocol, allowing centralized schema updates and validation logic without requiring client-side library updates

resume field extraction and structured parsing

Medium confidence

Solves for

Best for

Resume parsing and ATS (Applicant Tracking System) integration

Resume enrichment pipelines that need to normalize and structure raw resume data

Developers building resume import tools that accept text or markdown input

Requires

Node.js 16+

MCP client with text input capability

Resume content as plain text or markdown string

Limitations

Accuracy depends on resume formatting consistency — highly unstructured or non-standard layouts may have lower extraction accuracy

No OCR support — requires text input, not image or PDF scans

Field extraction is rule-based and may miss domain-specific or non-standard field types

What makes it unique

vs alternatives

Tighter integration with LLM workflows compared to standalone parsing libraries; agents can iteratively refine extraction by calling tools multiple times with different input variations

resume content enrichment and enhancement

Medium confidence

Solves for

Best for

Resume optimization tools and career coaching platforms

Job seekers using Claude or LLM agents to improve their resume quality

Resume-as-a-service platforms that need to enhance user-submitted content

Requires

Node.js 16+

MCP client with LLM capabilities (Claude or compatible model)

Existing resume data in JSON Resume format or as parsed fields

Limitations

Enrichment is generative and may introduce inaccuracies if not reviewed — requires human validation

No access to external job market data — suggestions are based on general best practices, not role-specific requirements

Cannot verify factual claims in resume content — enhancement is stylistic and structural only

What makes it unique

vs alternatives

Enables in-context resume improvement within Claude conversations, providing real-time suggestions and edits without context switching to external tools or platforms

multi-format resume export and generation

Medium confidence

Solves for

Best for

Resume management platforms that need to support multiple output formats

Developers building resume builders or portfolio tools

Teams managing resume templates and standardized export formats

Requires

Node.js 16+

MCP client with file output capability

Valid JSON Resume data structure as input

Limitations

PDF generation requires additional dependencies (e.g., puppeteer, wkhtmltopdf) — may add latency and system requirements

Template customization is limited to predefined themes — complex custom layouts require template modification

No support for advanced PDF features (forms, interactivity) — output is static document format

What makes it unique

vs alternatives

resume metadata and analytics extraction

Medium confidence

Solves for

Best for

Resume analysis and ATS optimization tools

Career coaching platforms that provide resume feedback

Job matching algorithms that need resume profile summaries

Requires

Node.js 16+

MCP client

Valid JSON Resume data with populated date fields

Limitations

Gap detection is date-based only — cannot distinguish intentional breaks (sabbatical, education) from unemployment

Skill analysis is frequency-based — does not assess skill relevance or market demand

Completeness scoring is based on field presence, not content quality or depth

What makes it unique

vs alternatives

Centralized analytics computation via MCP enables consistent analysis across all clients and allows agents to reason about resume profiles with derived metrics unavailable in raw resume data

resume validation with custom rule sets

Medium confidence

Solves for

Best for

Enterprise ATS systems with custom resume validation requirements

Recruitment platforms with standardized resume quality standards

Teams managing resume templates with specific content guidelines

Requires

Node.js 16+

MCP client

Rule set configuration (JSON or code-based)

Limitations

Rule configuration requires upfront definition — no automatic rule inference from examples

Custom validation logic is limited to predefined rule types — complex business logic requires code changes

No machine learning-based validation — rules are deterministic and require manual updates as standards evolve

What makes it unique

vs alternatives

Decouples validation rules from server implementation, allowing dynamic rule updates and client-specific validation policies without redeploying the MCP server

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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IntelliCode46Extension

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GitHub Copilot Chat49Extension

AI chat features powered by Copilot

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GitHub Copilot48Extension

Your AI pair programmer

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Claude Code for VS Code48Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

valjs-mcp-beta

Capabilities6 decomposed

json resume schema validation and transformation

resume field extraction and structured parsing

resume content enrichment and enhancement

multi-format resume export and generation

resume metadata and analytics extraction

resume validation with custom rule sets

Related Artifactssharing capabilities

@jsonresume/mcp

@mcpflow.io/mcp

@jsonresume/jsonresume-mcp

Brainner

Applaime

CoverQuick

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to valjs-mcp-beta

Are you the builder of valjs-mcp-beta?

Get the weekly brief

Data Sources

valjs-mcp-beta

Capabilities6 decomposed

json resume schema validation and transformation

resume field extraction and structured parsing

resume content enrichment and enhancement

multi-format resume export and generation

resume metadata and analytics extraction

resume validation with custom rule sets

Related Artifactssharing capabilities

@jsonresume/mcp

@mcpflow.io/mcp

@jsonresume/jsonresume-mcp

Brainner

Applaime

CoverQuick

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to valjs-mcp-beta

Are you the builder of valjs-mcp-beta?

Get the weekly brief

Data Sources