cvmaker-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@cvmaker-mcpGenerate a CV for a software engineer with 5 years experience at Google, using the modern template."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
cvmaker-mcp
A local Model Context Protocol (MCP) server that bridges AI assistants (Claude Desktop, Cursor, etc.) with the CV Maker app to generate pixel-perfect CV PDFs.
How it works
You describe your background to an AI assistant (e.g. Claude Desktop)
The AI structures your information and calls this MCP tool
The AI will explicitly ask where you want the final PDF saved, ensuring total control over the output destination.
A local headless browser securely visits resume.nigamelastic.com, computationally injects your CV data locally (without transmitting to any backend server), and captures the resume in PDF format.
In addition to PDFs, the AI can securely save or load your structured "profiles" as
.cv.jsonfiles directly to your hard drive, bypassing cloud storage entirely.
🔒 Privacy first: Your CV data is injected into a local browser's localStorage and is never sent to any external server.
Related MCP server: resume-mcp
Prerequisites
Node.js v18 or later
An MCP-compatible AI client:
Any client supporting the MCP stdio transport
Quick Start (No Install)
You can run the MCP server directly using npx in your MCP client configuration (Claude Desktop, Cursor, etc.):
{
"mcpServers": {
"cvmaker": {
"command": "npx",
"args": ["-y", "@nigamelastic/cvmaker-mcp"]
}
}
}Smart Browser Detection: The first time you run this, it will check for a system browser (Chrome/Brave/Chromium). If none is found, it will automatically download a lightweight version (~130MB) and notify you via logs.
Prerequisites
Node.js v18 or later
An MCP-compatible AI client:
Any client supporting the MCP stdio transport
Configuration
Claude Desktop
Add the following to your Claude Desktop config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.jsonLinux:
~/.config/Claude/claude_desktop_config.json
{
"mcpServers": {
"cvmaker": {
"command": "npx",
"args": ["-y", "@nigamelastic/cvmaker-mcp"]
}
}
}Development / Manual Installation
If you prefer to run from source:
git clone https://github.com/nigamelastic/cvmaker_mcp.git
cd cvmaker_mcp
npm install
node src/index.jsUsage
Once configured and Claude Desktop is restarted, you can use natural language:
"Create a CV for a Senior React Developer with 8 years of experience at Google and Meta. Use the tech template."
"Here's my LinkedIn summary: [paste text]. Build me a CV PDF using the elegant template."
Available Templates
Template | Description |
| Clean, traditional single-column layout |
| Contemporary design with accent colours |
| Ultra-clean, whitespace-focused |
| Sophisticated serif-accented layout |
| Two-column with a dark sidebar |
| Developer-focused, monospace accents |
| Traditional, highly structured European standardized layout |
| Formal, dense layout optimized for extensive senior experience |
Tool Reference
generate_cv_pdf
Generates a pixel-perfect A4 PDF using the CV Maker engine natively.
Parameter | Type | Required | Description |
| object | ✅ | Name, title, email, phone, website, location, summary |
| array | ✅ | List of |
| array | ✅ | List of |
| array | ✅ | List of skill strings |
| array | — | Up to 4 custom |
| string | — | Template name (default: |
| string | ✅ | Absolute directory path where you want the PDF saved |
| string | — | PDF filename without extension |
Returns: File path to the generated PDF on your local machine.
export_cv_json
Saves the structured CV JSON to a local file in ~/.cvmaker-profiles/. Perfect for saving "profiles" to iterate on later.
Parameter | Type | Required | Description |
| object | ✅ | structured CV JSON Payload |
| string | — | Desired profile filename without extension |
import_cv_json
Loads a previously saved CV JSON profile from your hard drive structure back into the AI context.
Parameter | Type | Required | Description |
| string | ✅ | Absolute absolute path to the |
extract_resume_data
A helper tool for validating AI-extracted resume/LinkedIn text explicitly against the strict CV JSON Schema before doing heavier rendering operations.
Parameter | Type | Required | Description |
| object | ✅ | structured CV JSON Payload |
get_available_templates
Fetches a list of valid template string IDs supported by CV Maker without requiring manual code inspection.
Warranty and Liability Disclaimer
This software is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall the authors or copyright holders be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.
Use of third-party site: This MCP server interacts with https://resume.nigamelastic.com/. While this server sanitizes data locally before injection, the user acknowledges that they are responsible for the content they process and that the final PDF rendering occurs within a headless browser instance.
License
MIT © nigamelastic
Available Tools
5 toolsexport_cv_jsonB
Save a generated CV to a local JSON file. This acts as a 'profile' you can reload later.
| Name | Required | Description | Default |
|---|---|---|---|
| cv | Yes | ||
| output_filename | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the core action (saving to local JSON) and the reloadable profile concept, but omits critical behavioral details: overwrite behavior, default output filename, return values, and error handling. This leaves significant unknowns for an agent invoking the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences, each earning its place: the first states the action, the second explains the purpose. No redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's nested cv object, optional filename, no output schema, and absence of annotations, the description is too sparse. It fails to explain the output_filename default, return behavior, or explicit compatibility with import_cv_json. An agent can infer the basic action but lacks enough context for correct invocation in all cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not mention either parameter. 'Generated CV' loosely maps to the 'cv' parameter, but the optional 'output_filename' is entirely unexplained. With 0% schema description coverage, the agent receives no guidance on parameter usage, defaults, or optionality.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Save') and resource ('a generated CV to a local JSON file'), clearly distinguishing it from siblings like generate_cv_pdf (PDF export) and import_cv_json (loading profiles). The phrase 'acts as a profile you can reload later' further reinforces its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool to save a CV as a reusable profile, implicitly contrasting with PDF generation and importing. It does not explicitly name alternatives or state when not to use, but the 'profile you can reload later' signals the intended use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
extract_resume_dataA
A helper tool for validating AI-extracted resume/LinkedIn text into a strict CV JSON format. Pass your organically constructed JSON against this to assert validity before generating PDFs or Profiles.
| Name | Required | Description | Default |
|---|---|---|---|
| cv | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must convey behavioral traits. It states this is a validation/assertion tool, implying a read-only, check-only operation. However, it doesn't disclose what happens on invalid input (e.g., error messages, return format), which is a significant gap for a validation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the key purpose ('validating') and providing a clear action directive. Every word earns its place, with no redundant filler or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema and no annotations, so the description needs to explain what the tool returns (e.g., a boolean, detailed errors, or list of validation issues). It also doesn't mention side effects or whether it modifies data. This lack of return-value and side-effect documentation makes the description incomplete for an agent to fully understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema is highly detailed with a single 'cv' parameter containing many nested required fields. The description adds context by explaining that the JSON comes from AI-extracted resume/LinkedIn text and must conform to a strict format, but it doesn't mention the parameter name or any property-level details. Since schema coverage is excellent, the description adds minimal semantic value beyond the schema itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: validating AI-extracted resume/LinkedIn text into a strict CV JSON format. It uses a specific verb ('validating') and resource ('CV JSON format'), and distinguishes it from siblings like generate_cv_pdf by framing it as a pre-generation validation step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool ('Pass your organically constructed JSON against this to assert validity before generating PDFs or Profiles'), which implies a clear workflow context. It doesn't enumerate alternatives, but the sibling names and 'before generating' make the usage timing unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_cv_pdfA
Generate a pixel-perfect PDF of a CV/resume using the CV Maker app at https://resume.nigamelastic.com/.
The tool accepts a structured CV JSON object and an optional template name, then:
Launches a local headless browser
Navigates to the live CV Maker site
Injects the CV data into the browser's localStorage (data never leaves this machine)
Waits for the React app to render the CV preview
Exports the rendered page as an A4 PDF with print backgrounds
Saves the PDF to a temp file and returns the file path
Available templates: standard, modern, minimal, elegant, sidebar, tech, europass, executive
| Name | Required | Description | Default |
|---|---|---|---|
| cv | Yes | ||
| destination_dir | Yes | Absolute path to the directory where the PDF will be saved. You MUST ask the user for this location before calling the tool (e.g., 'Where should I save this PDF?'). | |
| output_filename | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and delivers comprehensively. It details the step-by-step internal process (headless browser, localStorage injection, export to PDF) and notably includes the privacy statement 'data never leaves this machine.' This is high-value transparency beyond typical tool descriptions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence followed by a numbered list of steps and a template list. It is informative without being verbose. The step list adds behavioral detail, though it could be slightly more concise, but every sentence contributes to understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a complex tool with no output schema, so the description must explain return values and parameters. It mentions returning a file path but does not reconcile the destination_dir parameter or address output_filename. The conflict between saving to a temp file and the destination_dir parameter is a significant gap. Sibling tools are also not referenced, leaving the context incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 33%, and the description fails to compensate. It mentions the CV JSON and optional template name, but does not clarify destination_dir or output_filename. The step 'saves the PDF to a temp file' conflicts with the destination_dir parameter, adding confusion. The template list is helpful, but it does not address the semantic gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a pixel-perfect PDF of a CV/resume, specifying the verb (generate), resource (PDF), and the app URL. This unambiguously distinguishes it from sibling tools like export_cv_json and extract_resume_data, which handle JSON export or data extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context: use this when a PDF of a CV is needed. It specifies the input (structured CV JSON) and optional template, but does not explicitly exclude other tools or mention alternatives. The context is clear, but the lack of explicit alternative guidance keeps it from a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_available_templatesA
Fetches a list of valid template string IDs supported by CV Maker.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of explaining behavior. It indicates a read-only operation ('fetches') but does not disclose any additional traits such as authentication requirements, response format details, or whether the list is sorted. This leaves the agent with minimal behavioral context beyond the basic action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that delivers the core purpose without any filler. It is appropriately front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple no-parameter tool, the description is sufficient to inform the agent that it returns a list of template IDs. However, it could have added context about how these IDs are used with sibling tools (e.g., as input to generate_cv_pdf), which would make it more complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline score is 4. The description does not need to explain parameter meanings, and the schema is empty. No additional parameter semantics are required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'fetches' and clearly identifies the resource as 'list of valid template string IDs supported by CV Maker'. This distinguishes it from siblings like generate_cv_pdf or export_cv_json, which perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool is used to retrieve template IDs when needed, but it does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. The context signals show siblings with different purposes, so usage is indirectly clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
import_cv_jsonB
Load a previously saved CV JSON profile from your hard drive structure into the context.
| Name | Required | Description | Default |
|---|---|---|---|
| file_path | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits on its own. It states the load direction but does not mention error handling, whether existing context data is replaced, JSON validation behavior, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler words. It is concise, though it sacrifices some necessary behavioral detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and no output schema, but the description still lacks details about expected file structure, return values, and failure modes, leaving gaps for an agent deciding whether to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain the file_path parameter's format or constraints beyond 'from your hard drive structure', which weakly implies it is a path. This does not sufficiently compensate for the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action ('Load'), the resource ('previously saved CV JSON profile'), and the destination ('into the context'). It distinguishes itself from sibling tools like export_cv_json (which saves) and extract_resume_data (which parses).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage is clear: use this when you need to bring a previously exported CV JSON file into the current context. However, it does not explicitly mention when not to use it or name alternatives as a fallback.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
5 tool updates
v1.1.0- First observed
export_cv_json - First observed
extract_resume_data - First observed
generate_cv_pdf - First observed
get_available_templates - First observed
import_cv_json
TDQS
Each tool has a clearly distinct purpose: generating PDFs, saving/loading profiles, fetching templates, and validating CV data. There is no ambiguity between them, as the actions and inputs are well-differentiated.
All tool names follow a consistent verb_noun snake_case pattern (generate_cv_pdf, export_cv_json, import_cv_json, get_available_templates, extract_resume_data). This predictability makes the toolset easy to navigate.
With 5 tools, the server is well-scoped for its domain of CV generation and profile management. Every tool serves a necessary function without bloat, fitting comfortably within the ideal range.
The tool surface covers the full lifecycle: fetching templates, validating/creating CV data, generating PDFs, and persisting/reloading profiles. There are no missing operations that would hinder a typical agent workflow.
Maintenance
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