Local-first, multi-provider tool that captures LLM API spend and exposes it to coding agents via MCP
Local-first, multi-provider tool that captures LLM API spend and exposes it to coding agents via MCP
LLM Usage & Cost Tracker — your local-first spend watchdog
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Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| compare | Rank every priced model by projected cost for an n-input / m-output call. Cheapest first, percent against the cheapest. Default view family-deduplicates rows that share both a model family root *and* an identical price — |
| models | Catalog browser. Sibling of compare, but answers "what does this model charge?" rather than "what would my workload cost?". Rates per million tokens, sorted alphabetically by provider by default; switch with --sort input |
| recommend | Picks one. Filters by --provider, --model, and --budget, then returns the cheapest match plus two runner-ups. The reasoning string explains what it assumed and what got chosen, so you can sanity-check rather than trust b |
| spend | Read the SQLite. The default view is a usage_summary headline — total dollars, top-3 providers, top-3 models, largest single call. Pass --group-by to switch into rollup mode. |
| status | One screen, four sections: Database, Capture proxy, Providers, Pricing. The "is everything actually working?" command. Read-only — running it on a fresh install before you've ever booted the proxy or MCP server prints da |
| providers | Per-provider configuration view. Wider than the status Providers block: adds the wire-format flag (openai-compat: yes/no) and an optional --models expansion that lists every priced model under each provider. |
| about | The front-door panel: version, author, license, and the project homepage. The human-facing companion to --version — fields are read from the installed package metadata, so they match what PyPI shows. |
{
"mcpServers": {
"llm-usage": {
"command": "uvx",
"args": ["llm-usage-mcp"],
"env": {
"ANTHROPIC_API_KEY": "your-value",
"OPENAI_API_KEY": "your-value",
"DEEPSEEK_API_KEY": "your-value",
"DASHSCOPE_API_KEY": "your-value",
"ANTHROPIC_BASE_URL": "your-value",
"OPENAI_BASE_URL": "your-value",
"DEEPSEEK_BASE_URL": "your-value",
"LLM_USAGE_DB_URL": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| DEEPSEEK_API_KEY | Credential the server authenticates with. | Yes |
| DASHSCOPE_API_KEY | Credential the server authenticates with. | Yes |
| ANTHROPIC_BASE_URL | Endpoint or connection string the server talks to. | Yes |
| OPENAI_BASE_URL | Endpoint or connection string the server talks to. | Yes |
| DEEPSEEK_BASE_URL | Endpoint or connection string the server talks to. | Yes |
| LLM_USAGE_DB_URL | Endpoint or connection string the server talks to. | Yes |
Read, search and edit files inside allowed directories with the Model Context Protocol reference Filesystem server.
Retrieve version-aware library documentation and code examples with Context7 MCP, using resolve-library-id and query-docs.
Human authorization gateway for AI agent transactions
Python MCP Sandbox is an interactive Python code execution tool that allows users and LLMs to safely execute Python code and install packages in
MCP server for the Computer-Use Agent (CUA), allowing you to run CUA through Claude Desktop or other MCP clients.
The full sqbyl dev toolkit: introspect, profile, annotate, synth, eval, Coach, judges, console, optimizer, release builder.