Reasoning Engine MCP Server
Overview
The Reasoning Engine MCP server exposes tools for multi-step analytical workflows. These tools support correlation analysis, temporal pattern detection, pattern recognition, and structured explanation generation. The server is designed for complex analytical tasks that require coordinating multiple reasoning steps.
| Property | Value |
|---|---|
| Server ID | reasoning-engine |
| Tools | 8 |
| Streamable HTTP | /api/mcp/reasoning-engine/mcp when the deployed MCP transport is published |
| SSE | /api/mcp/reasoning-engine/sse when the deployed MCP transport is published |
The Reasoning Engine server is not included by default. It must be explicitly enabled by the platform operator. If you cannot connect to this server, confirm that it has been activated in the deployment configuration.
The source-backed server ID and tool list are current. The exact APIM MCP transport paths still require live testing before they are treated as a public integration contract.
Tools
| Tool ID | Description |
|---|---|
reasoning-engine-orchestrator | Orchestrate multi-step reasoning workflows |
reasoning-engine-normalize-checkin-dataset | Normalize check-in dataset for analysis |
reasoning-engine-multi-factor-correlation | Run multi-factor correlation analysis |
reasoning-engine-temporal-analysis | Perform temporal pattern analysis |
reasoning-engine-pattern-recognition | Identify patterns in data |
reasoning-engine-domain-retrieval | Retrieve domain-specific context |
reasoning-engine-explanation | Generate explanations for analysis results |
reasoning-engine-validation | Validate reasoning outputs |
Orchestrator Pattern
The orchestrator tool coordinates multi-step analytical workflows by invoking the specialized tools in sequence. Rather than calling each tool individually, you can submit an analysis request to the orchestrator and let it determine the appropriate sequence of steps.
How It Works
- Normalize -- The orchestrator first normalizes the input dataset into a consistent format using
reasoning-engine-normalize-checkin-dataset. - Retrieve Context -- Domain-specific context is loaded via
reasoning-engine-domain-retrievalto ground the analysis. - Analyze -- One or more analytical tools run in parallel depending on the request:
reasoning-engine-multi-factor-correlationfor cross-factor relationshipsreasoning-engine-temporal-analysisfor time-based patternsreasoning-engine-pattern-recognitionfor recurring structures in the data
- Explain -- The
reasoning-engine-explanationtool synthesizes findings into a structured explanation. - Validate -- The
reasoning-engine-validationtool checks the reasoning output for consistency and completeness.
Specialized Tools
Data Preparation
reasoning-engine-normalize-checkin-dataset-- Transforms raw check-in data into a normalized format suitable for analysis. Handles inconsistent field names, missing values, and type coercion.
Analysis
reasoning-engine-multi-factor-correlation-- Examines relationships between multiple variables in the dataset, identifying statistically significant correlations.reasoning-engine-temporal-analysis-- Detects trends, seasonality, and anomalies across time-series data.reasoning-engine-pattern-recognition-- Identifies recurring patterns and clusters within the data.
Context and Output
reasoning-engine-domain-retrieval-- Retrieves domain-specific reference data and context to inform the analysis.reasoning-engine-explanation-- Generates human-readable explanations of the analytical findings.reasoning-engine-validation-- Validates the consistency and completeness of reasoning outputs before they are returned.