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tokenectomy-razor
Advanced tools
Cut 95% of useless framework noise and token bloat from error logs before feeding them to AI coding agents
Autonomous, machine-to-machine (M2M) Model Context Protocol (MCP) server & log surgery engine for AI coding agents (Claude Desktop, Cursor, Cline, Roo Code, Windsurf, and Google Antigravity).
io.github.Tokenectomy-Labs/razorNo Rust compiler, no manual build, and zero dependencies required! The package automatically provisions the verified native precompiled Rust binary for your operating system and architecture:
npx -y tokenectomy-razor --mcp
x86_64 (glibc & musl), aarch64 (ARM64)arm64), Intel (x86_64)x86_64 (MSVC)Tokenectomy Razor equips agents with 5 high-performance tools adhering strictly to MCP JSON-RPC 2.0 specifications:
| Tool Name | Capability Description |
|---|---|
get_error_context | Performs deep polyglot trace surgery on error dumps, removes 90%+ framework noise (node_modules, site-packages, .cargo/registry, Spring Boot, Tomcat, Hibernate, ASan, Go idle goroutines), redacts credentials, and extracts local source context bounded to the workspace. |
analyze_code | Performs static AST code analysis to detect resource leaks, unclosed handles, and security vulnerabilities with bounded execution limits and precise LSP UTF-16 coordinates. |
apply_code_patch | Applies atomic file modifications with post-write language syntax verification (cargo check, node --check, py_compile) and automated zero-dirty-diff rollback on validation failure. |
search_stack_overflow | Queries Stack Exchange API for relevant error signatures using sanitized search terms. |
audit_context_health | Proactively audits error logs, prompt payloads, or code snippets for token bloat, framework noise, and credential leaks. Returns actionable M2M telemetry and cost savings metrics. |
Add to your claude_desktop_config.json:
{
"mcpServers": {
"tokenectomy": {
"command": "npx",
"args": ["-y", "tokenectomy-razor", "--mcp"]
}
}
}
.cursor/mcp.json)Add to .cursor/mcp.json in your workspace root:
{
"mcpServers": {
"tokenectomy": {
"command": "npx",
"args": ["-y", "tokenectomy-razor", "--mcp"]
}
}
}
Install directly into your Antigravity agent configuration:
agy mcp add tokenectomy -- npx -y tokenectomy-razor --mcp
Add to your MCP settings (cline_mcp_settings.json or settings.json):
{
"mcpServers": {
"tokenectomy": {
"command": "npx",
"args": ["-y", "tokenectomy-razor", "--mcp"]
}
}
}
| Metric | Measured Value | Standard / Guarantee |
|---|---|---|
| Redaction Throughput | 73.3 MB/sec (749,652 lines/sec) | 250,000-line enterprise dump processed in 333 ms |
| ReDoS Immunity | 1.44 ms | Linear $O(N)$ evaluation on 50K pathological backtracking patterns |
| Concurrent Throughput | 7,312 ops/sec | 100 concurrent OS threads across simultaneous sanitization calls |
| Memory Footprint | 76.24 MB VmRSS | Peak resident memory under continuous high-load stress |
You can also install the binary globally for standalone terminal CLI piping:
npm install -g tokenectomy-razor
# Run standalone CLI
razor --help
tokenectomy-razor --help
# Scrub framework frames and redact credentials via pipe
npm test 2>&1 | razor --scrub > sanitized.log
--proxy).analyze_code) & Glama.ai TDQS Grade A compliance.io.github.Tokenectomy-Labs/razor) & mcpservers.org directory listing.~/.tokenectomy.toml), interactive budgeting strategies.[:TOKENECTOMY:M2M_CONTROL_PLANE:v1.3.0]), Anti-Prompt-Injection Architecture, Downstream Liability Disclaimer.[DROPPED_FRAMES: ...]) to prevent silent truncation & content-addressable raw log verification hash.io.github.Tokenectomy-Labs/razor)FAQs
Cut 95% of useless framework noise and token bloat from error logs before feeding them to AI coding agents
The npm package tokenectomy-razor receives a total of 74 weekly downloads. As such, tokenectomy-razor popularity was classified as not popular.
We found that tokenectomy-razor demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.

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