TokenSaver
Structural warm start for AI coding agents in .NET: cheap Roslyn signature maps of C#/.NET files.
Install
Add this to your client's MCP configuration (Claude Desktop: claude_desktop_config.json; Cursor: .cursor/mcp.json; VS Code and others use the same "mcpServers" shape).
TokenSaver.Mcpclaude mcp add tokensaver -e 'TOKENSAVER_API_URL=<TOKENSAVER_API_URL>' -e 'TOKENSAVER_NO_TELEMETRY=<TOKENSAVER_NO_TELEMETRY>' -- dnx TokenSaver.Mcp@1.15.5 --yes{
"mcpServers": {
"tokensaver": {
"command": "dnx",
"args": [
"TokenSaver.Mcp@1.15.5",
"--yes"
],
"env": {
"TOKENSAVER_API_URL": "<TOKENSAVER_API_URL>",
"TOKENSAVER_NO_TELEMETRY": "<TOKENSAVER_NO_TELEMETRY>"
}
}
}
}Environment variables
TOKENSAVER_API_URLURL of the TokenSaver reporting dashboard. Defaults to https://tokensavermcp.com.TOKENSAVER_NO_TELEMETRYSet to 1 to opt out of anonymous usage telemetry.
Documentation
From the project's README on GitHub, refreshed weekly. Links open on GitHub.
Contents
TokenSaver
A structural warm start for AI coding agents in .NET. Instead of loading whole files into your assistant, TokenSaver hands it a cheap map of your code — every type and member as a signature, each tagged with its line range — so the model reads only the slice it needs instead of slurping the file. Built on the Roslyn compiler platform.
Where it pays off: outlining a file costs 70–95% fewer tokens than reading it — up to 90% on a large file. The end-to-end win is biggest on smaller / cheaper models (which over-read the most) and on large codebases: on real tasks it trims a Haiku-class model's token use by ~8%, and the savings climb with file size. A top-tier model already reads tightly, so it sees less benefit — the leaner the model, the more a warm start helps.
Works with Visual Studio 2026 (GitHub Copilot Chat), Claude Code, VS Code Copilot, Claude Desktop, and any other MCP client that speaks stdio.
→ Full docs and setup guide: mcp/README.md
→ Changelog: CHANGELOG.md
Install
See tokensavermcp.com/install for one-click install buttons, per-client config snippets, upgrade/uninstall instructions, and troubleshooting.
What the tools do
Single-file tools
| Tool | What it does | Reduction |
|---|---|---|
OutlineCSharpFile | Signatures of every type and member — no bodies. Best for "what's in this file?" | 70–95% |
MinifyFile | Lossless minify of an entire file — strips comments and whitespace, logic unchanged. Auto-dispatches by extension (C#, Razor, JS/TS, Python, HTML, CSS, JSON, YAML, XML, C, C++, X++, VB.NET). | 20–50% |
\* Reductions are measured against reading the whole file — the real alternative when you'd otherwise load it. The end-to-end saving on a task is smaller, because a capable model already reads somewhat selectively; it is largest on smaller/cheaper models and large files (see Token savings in practice below).
Cross-file traversal tools
These scan an entire project directory in one call — no need to know which file to look in first.
| Tool | What it does |
|---|---|
TraceDiRegistrations | Finds every Dependency-Injection registration referencing a type (interface or concrete) and returns a compact table of file:line, method, ServiceType -> ImplType, and key. Answers "where is IFoo wired, and to what?" |
Both accept a directory path or .csproj file — obj/ and bin/ are excluded automatically.
Token savings in practice
Measured against this project's own FocusedEmitter.cs (9,261 tokens raw):
| Question type | Tool | Tokens sent | Reduction |
|---|---|---|---|
| "What's in this file?" | OutlineCSharpFile | 1,039 | 89% |
| "Read one method body" | OutlineCSharpFile + narrow Read of the // L.. range | ~300 | ~95% |
| "Audit the whole file" | MinifyFile | 5,525 | 40% |
Those are per-read figures versus loading the whole file. The end-to-end saving on a real task is smaller, because a capable model already reads somewhat selectively — and that's the honest part of the story:
| Model running the agent | Real-task token saving vs no tool |
|---|---|
| Top-tier (reads tightly already) | ~1% — negligible |
| Mid-tier (Sonnet-class) | ~5–7% |
| Smaller / cheaper (Haiku-class) | ~8% |
The leaner the model, the more it over-reads on its own — and the more a warm start saves. TokenSaver is most worth it for agents running on small/cheap models, large files, or any workflow that would otherwise read whole files. It won't make a top model meaningfully cheaper, and it won't make a model write better code — it curbs wasteful reading, which is where cheap-model token budgets actually leak.
Collective impact
Every invocation is counted at tokensavermcp.com — a live dashboard showing total tokens saved by the community. Fewer tokens processed means less GPU compute and a smaller carbon footprint for AI-assisted development.
Language support
| Tier | Languages |
|---|---|
| Primary (Roslyn, full support) | C# .cs, Razor .razor, VB.NET .vb, .NET project files .csproj .props .config .xml |
| Basic (comment-strip + whitespace collapse) | JavaScript, TypeScript, Python, HTML, CSS/SCSS/LESS, JSON/JSONC, YAML, C, C++, X++ |
OutlineCSharpFile and TraceDiRegistrations are C# only; MinifyFile works across every supported language.
Frequently asked questions
What is TokenSaver?
Structural warm start for AI coding agents in .NET: cheap Roslyn signature maps of C#/.NET files.
How do I install TokenSaver?
It runs on your machine. In Claude Code: claude mcp add tokensaver -e 'TOKENSAVER_API_URL=<TOKENSAVER_API_URL>' -e 'TOKENSAVER_NO_TELEMETRY=<TOKENSAVER_NO_TELEMETRY>' -- dnx TokenSaver.Mcp@1.15.5 --yes. For other clients, paste the JSON configuration from this page into their MCP settings.
Does TokenSaver need an API key?
Its registry entry lists no required key or secret. Some features may still need an account with the service it connects to.
Is TokenSaver open source?
Yes. Its code is on GitHub under the MIT license.
Who maintains TokenSaver?
It is published by Byggarepop. AI.tn lists it but does not maintain or endorse it.
From the official MCP Registry. Listing is not an endorsement: review a server's code and permissions before giving it access to your data.