Give your LLM a cerebellum
reasoning.services connects your AI to structured reasoning tools via MCP — chain-of-thought, sequential analysis, decision trees, and more.
From prompt to structured output
Your LLM calls our MCP tools mid-generation. We return structured reasoning the model weaves into its response.
What are the second-order effects of remote work on commercial real estate?
Identifying direct effects: office vacancy increases, sublease supply surge, cap-rate compression in suburban markets.
Second-order: municipal tax base erosion, downtown retail decline, residential demand shift to exurbs.
Third-order: infrastructure investment redirection, school funding pressure in previously-commercial zones.
The cascading effect moves from balance sheets to city planning in roughly 18–36 months.
Drop in. Walk out with structure.
One MCP server. No SDKs. No custom prompts. Your existing Claude integration, extended.
// Add reasoning to any Claude call
const result = await claude.messages.create({
model: 'claude-opus-4-8',
max_tokens: 4096,
tools: mcpClient.tools(), // <-- one line
messages: [{ role: 'user', content: prompt }],
})$ npx @reasoning-services/mcp init
✓ MCP server connected
✓ 8 reasoning tools available
✓ Ready — claude.tools() returns them allAsk yours anything
What are the second and third-order effects of raising the minimum wage by 20% in a high-cost metro area?
The model that reasons well wins.
Eight tools. Every reasoning pattern.
Structured analysis
Simple. Transparent.
Monthly
Ready to give your LLM a cerebellum?
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