Agentfit

agentfit-mcp

MCP server for [`@mukundakatta/agentfit`](https://www.npmjs.com/package/@mukundakatta/agentfit). Lets Claude Desktop, Cursor, Cline, Windsurf, Zed, or any other MCP client estimate token counts and fit a chat history into a model's context budget on demand.

npx -y @mukundakatta/agentfit-mcp

Three tools:

• `count_tokens` — estimate tokens in a string or chat-message array, with per-model estimator families (openai, anthropic, google, llama, default).
• `fit_messages` — drop messages from a chat history until under a maxTokens budget. Supports drop-oldest, drop-middle, and priority strategies; honors preserveSystem, preserveFirstN, preserveLastN.
• `list_estimators` — list the built-in estimator families.

Add to your client

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{

"mcpServers": { "agentfit": { "command": "npx", "args": ["-y", "@mukundakatta/agentfit-mcp"] } } }

Cursor

~/.cursor/mcp.json:

{

"mcpServers": { "agentfit": { "command": "npx", "args": ["-y", "@mukundakatta/agentfit-mcp"] } } }

Cline / Windsurf / Zed

Same shape as above. The server speaks plain MCP over stdio, so any client that supports stdio MCP servers will work.

Tool examples

`count_tokens`:

{ "input": "hello world", "model": "claude-sonnet-4-6" }

Returns:

{ "tokens": 4, "model": "claude-sonnet-4-6" }

`fit_messages`:

{

"messages": [ { "role": "system", "content": "You are precise." }, { "role": "user", "content": "long context..." }, { "role": "assistant", "content": "..." }, { "role": "user", "content": "final question" } ], "maxTokens": 8000, "model": "claude-sonnet-4-6", "preserveSystem": true, "preserveLastN": 2, "strategy": "drop-oldest" }

Returns:

{

"messages": [...], "dropped": [...], "tokens": { "before": 12000, "after": 7800, "budget": 8000 }, "fit": true }

fit_messages always returns a structured result and never throws across the wire: if the budget is unreachable even after dropping all non-protected messages, you get fit: false with the partial result so the caller can decide what to do.

Why a separate MCP server

@mukundakatta/agentfit is a zero-dependency JavaScript library. This package wraps it as an MCP server so it's accessible from inside any MCP-aware AI assistant: ask Claude "how many tokens is this transcript?" or "trim this chat to 8k tokens preserving the system prompt and last 2 turns" and the assistant calls these tools directly.

Sibling MCP servers

Part of the agent-stack series, all @mukundakatta/*-mcp:

• @mukundakatta/agentfit-mcp — *Fit it.* (this)
• @mukundakatta/agentguard-mcp — *Sandbox it.*
• @mukundakatta/agentsnap-mcp — *Test it.*
• @mukundakatta/agentvet-mcp — *Vet it.*
• @mukundakatta/agentcast-mcp — *Validate it.*

License

MIT