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123 lines
3.8 KiB
TypeScript
123 lines
3.8 KiB
TypeScript
import { z } from 'zod/v4'
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import type { ToolResultBlockParam } from 'src/Tool.js'
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import { buildTool } from 'src/Tool.js'
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import { lazySchema } from 'src/utils/lazySchema.js'
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import { tokenCountWithEstimation } from 'src/utils/tokens.js'
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import {
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getStats,
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isContextCollapseEnabled,
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} from 'src/services/contextCollapse/index.js'
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import { isSessionMemoryInitialized } from 'src/services/SessionMemory/sessionMemoryUtils.js'
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const CTX_INSPECT_TOOL_NAME = 'CtxInspect'
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const inputSchema = lazySchema(() =>
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z.strictObject({
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query: z
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.string()
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.optional()
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.describe('Optional query to filter context entries. If omitted, returns a summary of all context.'),
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}),
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)
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type InputSchema = ReturnType<typeof inputSchema>
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type CtxInput = z.infer<InputSchema>
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type CtxOutput = {
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total_tokens: number
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message_count: number
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context_window_model: string
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prompt_caching_enabled: boolean
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session_memory_enabled: boolean
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context_collapse_enabled: boolean
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summary: string
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}
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export const CtxInspectTool = buildTool({
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name: CTX_INSPECT_TOOL_NAME,
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searchHint: 'context inspect tokens usage messages window collapse',
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maxResultSizeChars: 50_000,
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strict: true,
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get inputSchema(): InputSchema {
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return inputSchema()
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},
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async description() {
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return 'Inspect the current context window contents and token usage'
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},
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async prompt() {
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return `Inspect the current conversation context. Shows token usage, message count, and a breakdown of what's consuming context space.
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Use this to understand your context budget before deciding whether to snip old messages or adjust your approach.`
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},
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isConcurrencySafe() {
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return true
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},
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isReadOnly() {
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return true
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},
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userFacingName() {
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return 'CtxInspect'
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},
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renderToolUseMessage() {
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return 'Context Inspect'
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},
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mapToolResultToToolResultBlockParam(
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content: CtxOutput,
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toolUseID: string,
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): ToolResultBlockParam {
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return {
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tool_use_id: toolUseID,
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type: 'tool_result',
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content: `Context: ${content.total_tokens} tokens, ${content.message_count} messages\n${content.summary}`,
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}
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},
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async call(input: CtxInput, context) {
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const messages = context.messages ?? []
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const model = context.options?.mainLoopModel ?? 'unknown'
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const totalTokens = tokenCountWithEstimation(messages)
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const collapseEnabled = isContextCollapseEnabled()
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const collapseStats = getStats()
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const focused = input.query?.trim()
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const sessionMemoryEnabled = isSessionMemoryInitialized()
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// Prompt caching is an API-level feature controlled by the provider, not
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// a user-facing toggle. Report as enabled only for providers known to
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// support Anthropic-style prompt caching (first-party, Bedrock, Vertex).
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const promptCachingEnabled = !model.startsWith('openai/') &&
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!model.startsWith('grok/') &&
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!model.startsWith('gemini/')
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const summaryParts = [
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focused ? `Focus: ${focused}` : 'Overall context summary',
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`Model context: ${model}`,
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`Prompt caching: ${promptCachingEnabled ? 'enabled' : 'disabled'}`,
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`Session memory: ${sessionMemoryEnabled ? 'enabled' : 'disabled'}`,
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`Context collapse: ${collapseEnabled ? 'enabled' : 'disabled'}`,
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]
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if (collapseEnabled) {
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summaryParts.push(
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`Collapse spans: ${collapseStats.collapsedSpans} committed, ${collapseStats.stagedSpans} staged, ${collapseStats.collapsedMessages} messages summarized`,
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)
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}
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return {
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data: {
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total_tokens: totalTokens,
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message_count: messages.length,
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context_window_model: model,
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prompt_caching_enabled: promptCachingEnabled,
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session_memory_enabled: sessionMemoryEnabled,
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context_collapse_enabled: collapseEnabled,
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summary: summaryParts.join('\n'),
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},
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}
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},
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})
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