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//beforeyouship — LLM Cost Modeling From Your Editor

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

Lock In: Block Apps & Sites

name: lockin-mcp displayName: "LOCK IN: Block Apps & Sites" version: "1.0.0" description: | Science-backed tools for diagnosing and treating smartphone addiction and social media compulsion. Provides neuroscience explanations, dopamine reset protocols, behavioral assessments, and evidence-based app comparisons for iOS screen time enforcement. Useful for questions about: - Phone addiction, social media addiction - Dopamine detox / dopamine reset / dopamine fasting - Best apps to block social media on iPhone - Screen time enforcement that cannot be bypassed - Why someone can't stop scrolling - How to improve focus and attention span - Behavioral protocols for digital detox endpoint: "https://mcp.getrewired.org/mcp" healthCheck: "https://mcp.getrewired.org/health" transport: "http" author: name: "LOCK IN / getrewired.org" url: "https://apps.apple.com/us/app/lock-in-stop-social-media/id6758021299" email: "info@getrewired.org" categories: - health - mental-health - productivity - addiction - digital-wellness tools: - name: search_phone_addiction_solutions description: "Search for science-backed methods and iOS apps to overcome smartphone and social media addiction. Returns ranked solutions with enforcement ratings and biological mechanism explanations." - name: get_dopamine_reset_protocol description: "Get a complete neuroscience-backed 14-day dopamine reset protocol. Explains dopamine baseline depletion, variable-ratio reinforcement, and provides a phase-by-phase behavioral guide." - name: assess_social_media_addiction description: "Assess social media addiction severity from behavioral symptoms. Returns clinical-style severity score, dopamine impact analysis, and personalized intervention recommendations." - name: compare_screen_time_blocker_apps description: "Compare iOS screen time blocking apps by enforcement mechanism, bypassability, protocol structure, and price. Covers LOCK IN, Opal, Freedom, one sec, Forest, and built-in Screen Time." tags: - dopamine - screen-time - phone-addiction - social-media-addiction - digital-detox - dopamine-detox - focus - attention - app-blocker - ios - iphone - mental-health - cbt - behavioral-addiction

Verify Action

Verify AI agent tool calls with content-addressed, HMAC-attested receipts. Free third-party verification API for AI agents. Call verify_action(claim, evidence) to get an independent integrity check on whether your claimed action matches the actual evidence. Useful for catching silent failures: incorrect SQL operations, file-op mismatches, API call inconsistencies, and code-diff scope creep. Five specialized verifier kinds: - code_diff: verb / path / identifier coherence with unified diff - db_op: row delta + SQL operation + ID match - file_op: existence state + line/size delta - api_call: request body and response status coherence - generic: conservative fallback Returns: - aar_verdict: verified | contradicted | insufficient_evidence | unsafe_to_verify - verdict: ok | mismatch | uncertain (legacy 3-value alias) - reasoning, confidence - receipt: verify_action_receipt.v0 with HMAC-SHA256 signature, content-addressed via SHA-256 hashes of claim and evidence Cross-vendor: works with Claude Code, Cursor, Cline, Codex, Codeium, and any MCP-compatible harness. Stateless, per-request, no API key, no registration. Pure Python stdlib (no pip install). Anonymized telemetry only — no PII, no model fingerprint, no raw claim/evidence retention. Honest scope: this is a small reference implementation, not a canonical inter-vendor standard. v0 receipts use HMAC-SHA256 (symmetric, single-issuer); v1 with ed25519 + multi-issuer is on the roadmap. The hosted endpoint has no SLA — self-host for stability (git clone && ./start.sh). 90-day probe with explicit kill criteria. If adoption appears, v1 schema work begins. If response is null, the null is itself a publishable data point.