Context Window Packer
pack_context_windowSelects optimal content chunks to fit within LLM token limits using priority, greedy, or balanced strategies when content exceeds context window capacity.
Instructions
Pack content chunks into a token budget for an LLM context window. Selects the best subset of chunks that fits within the token limit using priority, greedy, or balanced strategies. Use when you have more content than fits in the context window.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| chunks | Yes | Content chunks to pack | |
| tokenBudget | Yes | Maximum tokens allowed | |
| model | No | Target model for tokenization | gpt-4o |
| strategy | No | Packing strategy | priority |
| separator | No | Text between chunks | |
| systemPrompt | No | System prompt to reserve tokens for | |
| reserveForOutput | No | Tokens to reserve for output |
Implementation Reference
- mcp-server/src/index.ts:1158-1198 (handler)The 'pack_context_window' tool is registered in mcp-server/src/index.ts, and its handler calls the Agent Toolbelt API endpoint 'context-window-packer'.
server.registerTool( "pack_context_window", { title: "Context Window Packer", description: "Pack content chunks into a token budget for an LLM context window. " + "Selects the best subset of chunks that fits within the token limit using priority, greedy, or balanced strategies. " + "Use when you have more content than fits in the context window.", inputSchema: { chunks: z.array(z.object({ text: z.string().describe("Content of this chunk"), label: z.string().optional().describe("Optional identifier"), priority: z.number().min(0).max(10).default(5).describe("Importance 0–10"), metadata: z.record(z.unknown()).optional(), })).describe("Content chunks to pack"), tokenBudget: z.number().int().describe("Maximum tokens allowed"), model: z.string().default("gpt-4o").describe("Target model for tokenization"), strategy: z.enum(["priority", "greedy", "balanced"]).default("priority").describe("Packing strategy"), separator: z.string().default("\n\n").describe("Text between chunks"), systemPrompt: z.string().optional().describe("System prompt to reserve tokens for"), reserveForOutput: z.number().int().min(0).default(0).describe("Tokens to reserve for output"), }, }, async ({ chunks, tokenBudget, model, strategy, separator, systemPrompt, reserveForOutput }) => { const result = await callToolApi("context-window-packer", { chunks, tokenBudget, model, strategy, separator, systemPrompt, reserveForOutput }); const data = result as any; const r = data.result; const lines = [ `**Packed ${r.stats.chunksPacked}/${r.stats.chunksTotal} chunks** | ${r.stats.tokensUsed}/${r.stats.effectiveBudget} tokens (${r.stats.utilizationPercent}% utilized)`, `**Strategy:** ${r.strategy} | **Model:** ${r.model}`, r.stats.systemPromptTokens > 0 ? `**System prompt:** ${r.stats.systemPromptTokens} tokens reserved` : "", r.stats.reservedForOutput > 0 ? `**Output reservation:** ${r.stats.reservedForOutput} tokens` : "", "", r.packed.length > 0 ? `**Packed chunks:**\n${r.packed.map((p: any) => `- ${p.label || `chunk[${p.originalIndex}]`} (priority ${p.priority}, ${p.tokens} tokens)`).join("\n")}` : "", r.excluded.length > 0 ? `\n**Excluded chunks:**\n${r.excluded.map((e: any) => `- ${e.label || `chunk[${e.originalIndex}]`} (${e.tokens} tokens, ${e.reason})`).join("\n")}` : "", ].filter(Boolean); return { content: [{ type: "text" as const, text: lines.join("\n") }] }; } );