MCP server for LLM token pricing data — query, compare, and estimate costs for 60+ AI models from OpenAI, Anthropic, Google, Meta, and more
If you already use Tokencost, the tokencost mcp server is the piece that lets your assistant work with it directly. MCP server for LLM token pricing data — query, compare, and estimate costs for 60+ AI models from OpenAI, Anthropic, Google, Meta, and more.
An MCP (Model Context Protocol) server that provides real-time LLM token pricing data for 60+ AI models across 15 providers.
npm on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
tokencost_get_model_pricing — Get pricing for a specific modeltokencost_compare_models — Side-by-side pricing comparisontokencost_estimate_cost — Calculate cost for given token countstokencost_find_cheapest — Find cheapest models with filterstokencost_list_models — List all available modelstokencost_list_providers — List all providers with pricing rangesPlenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. Tokencost's toolset — tokencost_get_model_pricing, tokencost_compare_models, tokencost_estimate_cost and 3 more — is a fair guide to whether it matches your workflow. It is maintained by ankit-aglawe; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| tokencost_get_model_pricing | Get pricing for a specific model |
| tokencost_compare_models | Side-by-side pricing comparison |
| tokencost_estimate_cost | Calculate cost for given token counts |
| tokencost_find_cheapest | Find cheapest models with filters |
| tokencost_list_models | List all available models |
| tokencost_list_providers | List all providers with pricing ranges |
{
"mcpServers": {
"tokencost": {
"command": "npx",
"args": ["-y", "tokencost-mcp-server"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.