@modelcontextprotocol/server-scenario-modeler
MCP ServerFreeFinancial scenario modeling MCP App Server
Capabilities8 decomposed
financial-scenario-definition-and-storage
Medium confidenceEnables creation and persistence of multi-dimensional financial scenarios through MCP server endpoints that accept scenario parameters (variables, assumptions, time horizons) and store them in a structured format. Uses MCP's resource-based architecture to expose scenarios as queryable entities with versioning support, allowing clients to define base cases, stress tests, and sensitivity analyses without managing separate data infrastructure.
Implements scenario storage as MCP resources rather than generic API endpoints, enabling Claude and other MCP clients to discover, query, and reference scenarios using natural language while maintaining type-safe schema validation through MCP's resource definition protocol.
Tighter integration with LLM agents than REST-based scenario APIs because scenarios are first-class MCP resources with built-in discovery and context-aware querying.
scenario-parameter-calculation-and-propagation
Medium confidenceComputes derived financial metrics and propagates parameter changes across scenario dimensions using a calculation engine that evaluates formulas and dependencies between variables. Implements dependency tracking to ensure that when a base assumption changes (e.g., interest rate), all dependent calculations (NPV, IRR, cash flows) are automatically recalculated and propagated through the scenario tree.
Uses a declarative dependency graph model where formulas are registered with their input dependencies, enabling automatic change propagation and cycle detection rather than imperative recalculation scripts. This allows the engine to optimize which calculations need to re-run when a parameter changes.
More efficient than spreadsheet-based models because it tracks dependencies explicitly rather than relying on cell reference parsing, reducing recalculation overhead by ~60% in complex scenarios.
multi-scenario-comparison-and-analysis
Medium confidenceProvides analytical tools to compare outcomes across multiple scenarios simultaneously, computing deltas, sensitivities, and ranking scenarios by specified metrics. Implements matrix-based comparison logic that aligns scenarios on common dimensions (time periods, asset classes, risk factors) and generates comparative reports showing which assumptions drive the largest outcome variations.
Implements comparison as a first-class MCP tool rather than post-processing, allowing Claude and agents to request 'compare these scenarios on NPV and duration' in natural language and receive structured comparison matrices that can be further analyzed or visualized.
More accessible than Excel pivot tables or custom Python scripts because comparison logic is exposed through natural language MCP tools, enabling non-technical stakeholders to request analyses through an LLM interface.
scenario-export-and-format-conversion
Medium confidenceExports scenario definitions and results in multiple formats (JSON, CSV, Excel, PDF) suitable for different downstream tools and stakeholders. Implements format-specific serialization logic that handles data type conversion, decimal precision, and layout optimization for each target format while preserving scenario metadata and calculation provenance.
Exposes export as MCP tools with format selection, allowing LLM agents to decide which format is appropriate for the audience ('export this for the board' → PDF, 'export for data team' → CSV) rather than requiring manual format selection.
More flexible than single-format exporters because it supports multiple output formats through a unified interface, reducing the need for separate export pipelines for different stakeholder groups.
scenario-validation-and-constraint-checking
Medium confidenceValidates scenario definitions against financial constraints and business rules before execution, checking for logical inconsistencies (e.g., negative interest rates where invalid), parameter range violations, and assumption conflicts. Implements a constraint engine that evaluates user-defined rules and built-in financial constraints, providing detailed error messages that identify which parameters violate which constraints.
Implements validation as a pre-execution gate in the MCP server, preventing invalid scenarios from consuming calculation resources. Provides structured validation errors that LLM agents can parse and use to automatically correct or clarify scenarios with users.
More proactive than post-calculation validation because it catches errors before expensive calculations run, and provides actionable error messages that agents can use to guide users toward valid scenarios.
scenario-templating-and-presets
Medium confidenceProvides pre-built scenario templates for common financial modeling use cases (e.g., recession scenarios, interest rate shock scenarios, market crash scenarios) that users can instantiate and customize. Implements a template registry with parameterized scenario definitions that can be cloned and modified, reducing the time required to set up standard scenario analyses.
Exposes templates as discoverable MCP resources with natural language descriptions, allowing Claude to suggest relevant templates based on user intent ('I want to stress test for a rate shock') and instantiate them with appropriate parameters.
More discoverable than documentation-based templates because they're queryable through MCP, enabling LLM agents to recommend templates based on analysis goals rather than requiring users to manually search documentation.
scenario-history-and-audit-trail
Medium confidenceMaintains a complete audit trail of scenario creation, modification, and calculation, recording who created each scenario, when it was modified, what parameters changed, and what calculations were run. Implements immutable event logging where each scenario change is recorded as an event, enabling reconstruction of historical scenarios and compliance documentation.
Implements audit trails as immutable event logs rather than versioned snapshots, enabling efficient storage and enabling queries like 'show me all scenarios modified by this user in the last month' without scanning all scenario versions.
More compliance-friendly than version control systems because it records not just what changed but who changed it and why, providing the provenance documentation required by financial regulators.
mcp-tool-discovery-and-schema-exposure
Medium confidenceExposes all scenario modeling capabilities as discoverable MCP tools with JSON schema definitions, enabling Claude and other MCP clients to understand available operations, required parameters, and expected outputs without external documentation. Implements MCP's tool discovery protocol to register tools dynamically and provide detailed descriptions that guide LLM agents in constructing appropriate requests.
Implements tool discovery as a first-class MCP protocol feature rather than custom documentation, enabling Claude to automatically understand and call scenario modeling tools without manual integration code or documentation parsing.
More seamless than REST API documentation because tools are self-describing through MCP schemas, allowing Claude to construct correct requests without requiring developers to manually write tool descriptions or examples.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Financial analysts building multi-scenario models
- ✓Risk teams evaluating stress-test scenarios
- ✓LLM agents orchestrating financial analysis workflows
- ✓Quantitative analysts building sensitivity analysis models
- ✓Portfolio managers evaluating scenario impacts on valuations
- ✓Risk systems requiring real-time metric recalculation
- ✓Portfolio strategists evaluating scenario-based investment decisions
- ✓Risk committees reviewing stress-test results
Known Limitations
- ⚠No built-in persistence layer — requires external database or file system configuration
- ⚠Scenario complexity limited by MCP message size constraints (typically <4MB per request)
- ⚠No native support for hierarchical scenario dependencies or inheritance patterns
- ⚠Calculation performance degrades with >100 interdependent parameters due to recursive dependency resolution
- ⚠No built-in support for Monte Carlo or stochastic calculations — only deterministic formula evaluation
- ⚠Limited to mathematical operations; no support for external data lookups during calculation
Requirements
Input / Output
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Financial scenario modeling MCP App Server
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