a869f77070
Merges feat/agent-project-membership (task 166/152). An agent can finally join many projects: membership routes, per-membership role and optional brain override, chat resolving context from the membership, and CLI attach-agent / detach-agent / get memberships. The legacy Agent.projectId fallback is kept for this release as the schema comment promises. Conflict resolved in main.ts: that branch added its RBAC case to mapUrlToPermission inline, while this branch had extracted the function to middleware/url-permission.ts so it could be tested. Kept the extraction and ported the case into it — joining a team is `edit:agents:<name>`, not `create:agents`, which is the authority to mint a whole agent. Also fixes something the extraction makes possible. security.test.ts contained a hand-copied reproduction of mapUrlToPermission, because main.ts self-invokes main() and nothing in it was importable. A security test asserting against a copy stops testing the system the moment the real one changes, and this copy had already drifted. It now imports the real function; all 18 assertions still pass, against the code that actually runs. Local: build clean, workspace 2550 passed, lint unchanged at 794 errors. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01N4wNHWf7xSwnZCWpJcyv9p
71 lines
2.3 KiB
Markdown
71 lines
2.3 KiB
Markdown
# Task ID: 32
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**Title:** Implement smart context optimization
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**Status:** done
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**Dependencies:** 29 ✓
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**Priority:** medium
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**Description:** Add token counting and decision logic to intelligently skip LLM filtering when responses are small enough, and cache filtering decisions for repeated queries.
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**Details:**
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1. Create `src/mcplocal/src/llm/token-counter.ts`:
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```typescript
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export function estimateTokens(text: string): number {
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// Simple estimation: ~4 chars per token for English
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// More accurate: use tiktoken or similar library
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return Math.ceil(text.length / 4);
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}
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```
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2. Create `src/mcplocal/src/llm/filter-cache.ts`:
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```typescript
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export class FilterCache {
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private cache: LRUCache<string, FilterDecision>;
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shouldFilter(toolName: string, params: unknown, responseSize: number): boolean {
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const key = this.computeKey(toolName, params);
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const cached = this.cache.get(key);
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if (cached) return cached.shouldFilter;
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// No cache hit - use default threshold logic
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return responseSize > this.tokenThreshold;
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}
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recordDecision(toolName: string, params: unknown, decision: FilterDecision): void {
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const key = this.computeKey(toolName, params);
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this.cache.set(key, decision);
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}
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}
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```
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3. Add configuration options:
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- `tokenThreshold`: number (default 1000 tokens)
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- `filterCacheSize`: number (default 1000 entries)
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- `filterCacheTtl`: number (default 3600 seconds)
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4. Integrate into LlmProcessor:
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```typescript
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async filterResponse(...) {
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const tokens = estimateTokens(JSON.stringify(rawResponse));
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if (tokens < this.config.tokenThreshold) {
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// Not worth filtering - return as-is
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return { filtered: false, response: rawResponse };
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}
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// Proceed with LLM filtering
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}
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```
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5. Add metrics tracking:
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- Total tokens processed
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- Tokens saved by filtering
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- Filter cache hit rate
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- Average latency added by filtering
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**Test Strategy:**
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1. Unit test: Token estimation is reasonably accurate
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2. Unit test: Cache correctly stores and retrieves decisions
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3. Unit test: Threshold logic skips filtering for small responses
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4. Unit test: Cache TTL expiration works correctly
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5. Integration test: Metrics are recorded accurately
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6. Performance test: Cache improves latency for repeated queries
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