DeepSeek

@DeepSeek list all available models and tell me which one is best for reasoning tasks that require step-by-step logic

I found 2 models currently available: deepseek-chat (general-purpose conversational model) and deepseek-reasoner (optimized for multi-step reasoning with explicit thinking process). For reasoning tasks requiring step-by-step logic, deepseek-reasoner is the better choice — it exposes its internal reasoning chain, making it ideal for complex problem-solving, mathematical proofs, or tasks where you need to audit the AI's thought process. deepseek-chat works well for standard conversational use cases.

This example shows the MCP's model discovery capability. Useful for understanding which DeepSeek model fits your use case before making API calls. The model list reflects DeepSeek's current offerings and may expand over time — check periodically if you're building long-term integrations.

@DeepSeek check my account balance and let me know if I have enough credit to run about 50 reasoning-heavy queries this week

Your DeepSeek account shows a total balance of $12.40 USD (granted: $10.00, topped-up: $2.40). Based on typical deepseek-reasoner pricing (~$0.15-0.30 per reasoning query depending on length), you have enough credit for approximately 40-80 queries. You're close to the lower end of your 50-query target — consider topping up $5-10 if you want headroom for longer or more complex prompts.

This demonstrates balance checking before committing to a workload. The MCP returns both granted (promotional) and paid balances. Cost estimates here are illustrative — actual usage depends on prompt/response length. Monitor balance regularly if you're running automated workflows to avoid mid-task interruptions.

@DeepSeek using deepseek-reasoner, walk through the logic of whether we should migrate our API from REST to GraphQL given we have 12 endpoints and 3 mobile clients

I invoked deepseek-reasoner with your migration question. The model's reasoning chain considered: (1) REST simplicity vs. GraphQL flexibility trade-off, (2) overhead of maintaining a GraphQL schema for only 12 endpoints, (3) mobile client benefits (reduced over-fetching, single request for related data), and (4) team learning curve. Conclusion: GraphQL likely provides marginal benefit at your scale — the schema overhead and team ramp-up cost outweigh the mobile efficiency gains unless you're planning significant endpoint growth (20+ endpoints) or have severe over-fetching issues now. The full reasoning trace is available if you want to challenge specific assumptions.

This showcases the MCP's ability to invoke DeepSeek's reasoning model and surface its step-by-step thought process. The 'thinking mode' makes the AI's logic auditable, which is valuable for high-stakes decisions. Be aware: reasoning queries consume more tokens than standard chat, so they cost more per call. Use for complex problems, not simple lookups.

When DeepSeek wins for cost-sensitive prototyping

A 3-person startup building an AI feature needs to test prompt variations without burning through their OpenAI credits. DeepSeek's balance-checking tool and lower per-token pricing make it the right call here—you can iterate on 50 prompt variants in a day and still stay under $10. The Anthropic-compatible format means you're not locked in; swap the API key back to Claude when you're ready to ship. The trade-off: DeepSeek's reasoning mode is slower than GPT-4o for latency-critical features, so if your prototype needs sub-second responses in production, plan to migrate. If you're optimizing for learning cost over speed, this MCP pays for itself in week one.

When this MCP fits model evaluation workflows

A 6-person ML team is benchmarking reasoning quality across providers for a legal document summarization tool. DeepSeek's MCP lets them run the same prompt through deepseek-reasoner and deepseek-chat, then compare outputs against Claude and GPT-4 in a single Switchy workspace. The list-models tool surfaces version changes without checking docs. The catch: if your eval set is over 500 prompts, you'll hit rate limits faster than with enterprise-tier OpenAI, and the balance tool only helps if you're on DeepSeek's prepaid plan. For teams running weekly model evals on <200 samples, this MCP cuts setup time from 30 minutes to 5. If you're doing continuous eval at scale, you need a provider with higher throughput.

When DeepSeek handles high-volume ticket replies

A 10-person support team at a SaaS company drafts 200+ ticket responses daily using templated prompts. DeepSeek's chat completion tool with streaming gives agents real-time feedback as answers generate, and the cost per response is 60% lower than GPT-4. The balance-check tool prevents mid-shift outages when prepaid credits run low. The boundary: if your tickets require tool-calling to pull live data from Zendesk or Stripe, DeepSeek's function-calling support is less mature than OpenAI's—expect to write more error-handling code. For pure text generation at volume, where speed matters more than complex integrations, this MCP delivers ROI in month one. If your workflow needs 5+ tool calls per response, stick with a provider built for agentic use cases.