Productivity
maxcv.ai
Helps match and tailor CVs to job postings for improved job applications.
ENDPOINT 1
https://maxcv.ai/mcp
MCP server metadata
- Name
- maxcv
- Version
- 0.1.0
maxcv tailors an existing CV to a specific job posting. Call score_cv to show the match gap, then tailor_cv to rewrite the CV honestly (no fabricated skills). Trial is rate-limited; for unlimited use sign up at https://maxcv.ai.
Known tools 2
tailor_cvTailor a CV/resume to a specific job posting: rewrites the CV with the posting's ATS keywords (never fabricating skills not already present), and returns the tailored CV, a match score, role-fit notes and interview prep.
Inferred read-onlyCONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.maxcv]
url = "https://maxcv.ai/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"maxcv": {
"type": "http",
"url": "https://maxcv.ai/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: maxcv
Remote MCP URL: https://maxcv.ai/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"maxcv": {
"url": "https://maxcv.ai/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"maxcv": {
"type": "http",
"url": "https://maxcv.ai/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "maxcv",
"transport": "streamable-http",
"url": "https://maxcv.ai/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
TRUST AND VERIFICATION EVIDENCE
Trust Data Available
BuiltWith Trust API v2 evidence for maxcv.ai was fetched 2026-08-03T20:32:11.294Z and is being refreshed.
maxcv.ai is assessed as Trusted: Domain runs a meaningful technology spend, consistent with a real business.
Evidence is source-attributed and does not guarantee that a third-party server is safe. Risk labels are conservative metadata heuristics.