mcpField
A
Analytical MCP Server

Analytical MCP Server

Local
github.com/quanticsoul4772Data & Analytics4Updated 3d ago

About Analytical MCP Server

Analytical MCP Server exposes 12 tools for statistical analysis, decision support, logical reasoning, and research verification. It is for Claude users who need structured analytical operations; most processing runs locally, while research tools use Exa and require an API key.

Key features

  1. 01Descriptive statistics and correlation
  2. 02Regression and hypothesis testing
  3. 03Data preprocessing and chart specifications
  4. 04Model evaluation and weighted decisions
  5. 05Argument and fallacy analysis
  6. 06Exa-backed research verification

Use cases

  1. 01Summarize and compare dataset variables
  2. 02Test hypotheses or fit regression models
  3. 03Rank options against weighted criteria
  4. 04Check claims across research sources

MCP support

It communicates with MCP clients over stdio and registers 12 analytical tools. The research verification and perspective tools call Exa and require EXA_API_KEY.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Analytical MCP Server

analytical-mcp MCP server

Model Context Protocol server exposing 12 analytical tools for Claude: descriptive statistics, regression (linear/polynomial/logistic/multivariate), hypothesis testing, data preprocessing, data visualization specs, multi-criteria decision analysis, ML model evaluation, logical argument/fallacy analysis, and Exa-backed research verification. Runs over stdio; the analytical core needs no API key (research features require EXA_API_KEY).

Setup

Prerequisites
  • Node.js >= 20.0.0
  • EXA_API_KEY environment variable (required for verify_research and perspective_shifter, both of which call the Exa search API on every invocation)
Installation
Option 1: Direct Installation
npm install
npm run build
Option 2: Docker

Build the image. The server speaks the MCP protocol over stdio — it is launched (and its stdin/stdout piped) by the MCP client, not run as a detached daemon; see the Docker entry under Configuration for how Claude Desktop invokes it.

docker build -t analytical-mcp .

# Smoke-test the image interactively (Ctrl-C to exit):
docker run --rm -i -e EXA_API_KEY=your_api_key_here analytical-mcp
Configuration
Direct Installation Configuration
  1. Copy .env.example to .env
  2. Add your EXA_API_KEY to .env
  3. Add to Claude Desktop configuration:
{
  "mcpServers": {
    "analytical": {
      "command": "node",
      "args": ["/path/to/analytical-mcp/build/index.js"],
      "env": {
        "EXA_API_KEY": "your-exa-api-key-here"
      }
    }
  }
}
Docker Configuration
  1. Copy .env.example to .env
  2. Add your EXA_API_KEY to .env
  3. Add to Claude Desktop configuration:
{
  "mcpServers": {
    "analytical": {
      "command": "docker",
      "args": [
        "run", "--rm", "-i",
        "--env-file", ".env",
        "-v", "$(pwd)/cache:/app/cache",
        "analytical-mcp"
      ]
    }
  }
}

Available Tools

The server registers 12 tools on startup, unconditionally (registration does not depend on EXA_API_KEY; the two research-backed tools below will error at call time if the key is missing). See src/tools/index.ts for the authoritative list.

Statistical Analysis
  • analyze_dataset — Descriptive statistics for a numeric or record-array dataset (summary/stats).
  • advanced_statistical_analysis — Descriptive statistics plus cross-variable Pearson correlation on tabular data (arrays of objects). Use analyze_dataset for a single numeric series.
  • advanced_regression_analysis — Linear, polynomial, logistic, and multivariate regression, backed by dedicated provider modules with real OLS/logistic math (not mocked).
  • hypothesis_testing — Real statistical hypothesis tests: Welch's independent t-test, paired t-test, correlation, chi-square, and ANOVA, using exact p-value computation (see src/utils/statistics.ts).
  • advanced_data_preprocessing — Normalization, standardization, missing-value handling, and IQR outlier detection on numeric data.
  • data_visualization_generator — Generate chart specifications (scatter, line, bar, histogram, box, heatmap, pie, violin, correlation).
Machine Learning
  • ml_model_evaluation — Evaluate model predictions: classification metrics (accuracy, precision, recall, F1) or regression metrics (MSE, MAE, RMSE, R²).
Decision Analysis
  • decision_analysis — Multi-criteria weighted decision ranking. Requires a scores matrix (options.length rows × criteria.length columns, each value 0-10) in addition to options and criteria; weights is optional and defaults to equal weighting. This is a breaking requirement versus older docs that only described options/criteria/weights.
Logical Reasoning
  • logical_argument_analyzer — Analyze argument structure, fallacies, validity, and strength (via dedicated provider classes).
  • logical_fallacy_detector — Detect and explain logical fallacies in text with confidence scoring.
  • perspective_shifter — Generate alternative perspectives (stakeholder, discipline, contrarian, optimistic, pessimistic) on a problem. Requires EXA_API_KEY: it runs an Exa search per perspective domain to ground each perspective.
Research Verification
  • verify_research — Cross-verify research claims from multiple sources. Requires EXA_API_KEY. Returns confidence.score (the actual computed consistency/confidence value, 0-1) and confidence.verified (boolean: whether confidence.score met minConsistencyThreshold) — the threshold is a pass/fail cutoff, never a floor applied to the reported score.

Observability & Metrics

The Analytical MCP Server includes a built-in Prometheus-style metrics HTTP server (src/utils/metrics_server.ts) for monitoring cache performance and system health.

Metrics Endpoint

When enabled, the server exposes metrics via HTTP on port 9090 (configurable):

  • http://localhost:9090/metrics - Prometheus-style metrics
  • http://localhost:9090/metrics?format=json - JSON format metrics
  • http://localhost:9090/health - Health check endpoint
  • http://localhost:9090/ - Metrics server status page
Available Metrics
Cache Metrics
  • analytical_mcp_cache_hits_total - Cache hits by namespace
  • analytical_mcp_cache_misses_total - Cache misses by namespace
  • analytical_mcp_cache_puts_total - Cache puts by namespace
  • analytical_mcp_cache_evictions_total - Cache evictions by namespace
  • analytical_mcp_cache_size - Current cache size by namespace
System Metrics
  • analytical_mcp_uptime_seconds - Server uptime in seconds
  • analytical_mcp_memory_usage_bytes - Memory usage (RSS, heap, external)
  • analytical_mcp_cpu_usage_microseconds - CPU time usage (user, system)
Configuration

Enable metrics by setting environment variables:

METRICS_ENABLED=true        # Enable metrics server (default: false; unauthenticated, opt-in)
METRICS_PORT=9090          # Metrics server port (default: 9090)
METRICS_HOST=127.0.0.1     # Metrics server host (default: 127.0.0.1, use 0.0.0.0 to bind to all interfaces)
Usage Examples
# Get Prometheus metrics
curl http://localhost:9090/metrics

# Get JSON metrics
curl http://localhost:9090/metrics?format=json

# Health check
curl http://localhost:9090/health
Audit logging

Every tool call emits one structured audit record to stderr (never stdout — that is the MCP protocol channel), independent of LOG_LEVEL:

[2026-07-05T22:56:12.629Z] AUDIT: {"event":"tool_call","tool":"verify_research","ok":true,"durationMs":1352,"argBytes":120,"argHash":"31b769fe1f66","exaCalls":2}

Each record carries the tool name, outcome, duration, the byte size and a SHA-256 fingerprint of the arguments (never the raw argument values, so no content is leaked), and exaCalls — the number of outbound Exa requests the call issued. This gives an operator a forensic trail (oversized or repeated inputs, unexpected external fan-out) without recording sensitive content. It is gated by a single flag, on by default and independent of LOG_LEVEL:

ENABLE_AUDIT_LOG=true   # per-call audit records to stderr (default: true)

Usage Examples

Dataset Analysis
{
  "data": [23, 45, 67, 12, 89, 34, 56, 78],
  "analysisType": "stats"
}
Decision Analysis
{
  "options": ["Option A", "Option B", "Option C"],
  "criteria": ["Cost", "Quality", "Speed"],
  "scores": [
    [7, 6, 8],
    [5, 9, 6],
    [9, 4, 7]
  ],
  "weights": [0.4, 0.4, 0.2]
}
Hypothesis Testing
{
  "testType": "t_test_independent",
  "data": [[23, 45, 67, 12, 89], [34, 56, 78, 90, 21]],
  "alpha": 0.05
}
Logical Analysis
{
  "argument": "All birds can fly. Penguins are birds. Therefore, penguins can fly.",
  "analysisType": "comprehensive"
}

Development

Testing
# Run the offline unit suite (no API key needed) — same as test:unit
npm test

# Unit tests only (offline, no API key needed)
npm run test:unit

# Integration tests (non-blocking heads-up without EXA_API_KEY; live-Exa cases self-skip)
npm run test:integration

# Integration tests excluding the live-API suite
npm run test:integration:no-api

# Or via the thin wrapper script
./tools/test-runner.sh unit
./tools/test-runner.sh integration
./tools/test-runner.sh integration:no-api
Scripts
  • npm run build - Build TypeScript to JavaScript
  • npm run watch - Watch for changes and rebuild
  • npm run typecheck - Type-check src/ (excludes test files)
  • npm run typecheck:src - Type-check src/ plus integration tests
  • npm run lint / npm run lint:fix - ESLint
  • npm run format / npm run format:check - Prettier
  • npm test / npm run test:unit / npm run test:integration - Jest (see Testing)
  • npm run smoke - Builds, starts the real server, and drives initialize/tools-list/tools-call over stdio JSON-RPC
  • npm run cache:stats / cache:clear / cache:preload - Manage the on-disk research cache
  • npm run inspector - Start MCP inspector for debugging
Project Structure
analytical-mcp/
├── src/
│   ├── tools/           # MCP tool implementations (12 registered tools + supporting providers)
│   ├── utils/           # Utility functions, regression/NLP providers, caching, resilience, metrics
│   ├── integration/     # Integration tests (live-Exa cases self-skip without EXA_API_KEY)
│   ├── __tests__/       # Server-level protocol test (InMemoryTransport)
│   └── index.ts         # Main server entry point
├── docs/                # Documentation
├── tools/               # Development and testing scripts
├── scripts/             # Build/smoke-test scripts
└── examples/            # Usage examples

Architecture Notes

  • Provider architecture: Complex tools (regression, NLP, visualization, argument analysis) are decomposed into single-responsibility provider modules in src/utils/ and src/tools/ (e.g. linear_regression_provider.ts, logistic_regression_provider.ts, polynomial_regression_provider.ts, multivariate_regression_provider.ts, regression_metrics_provider.ts). Tool files orchestrate and format; providers hold the logic.
  • Resilience: src/utils/rate_limit_manager.ts handles Exa rate limiting (key rotation, per-endpoint throttling); src/utils/api_helpers.ts provides retry with an explicit shouldRetry predicate.
  • Caching: src/utils/cache_manager.ts, src/utils/enhanced_cache.ts, and src/utils/research_cache.ts provide layered, namespace-aware caching (enable with ENABLE_RESEARCH_CACHE=true).
  • Statistics: src/utils/statistics.ts implements log-gamma, incomplete beta/gamma, and t/F/chi-square CDFs from first principles for exact p-value computation — no statistical approximations or mocked results.

Tool Categories

Statistical Analysis
  • Descriptive statistics: mean, median, standard deviation, quartiles
  • Regression analysis: linear, polynomial, logistic, multivariate
  • Hypothesis testing: Welch t-test, paired t-test, correlation, chi-square, ANOVA
Decision Support
  • Multi-criteria weighted decision ranking from an explicit options × criteria score matrix
Logical Reasoning
  • Argument structure, validity, and strength analysis
  • Fallacy detection with confidence scoring
  • Perspective generation
Research Integration
  • Multi-source verification via Exa
  • Fact extraction
  • Conflict/consistency checking
  • Confidence scoring

Security and Privacy

  • All analytical processing occurs locally
  • Research features use the Exa API (optional, requires EXA_API_KEY)
  • No permanent data storage beyond the optional local disk cache
  • API keys managed via environment variables

License

MIT License. See LICENSE file for details.

Contributing

See CONTRIBUTING.md for the contribution workflow and conventions, and docs/DEVELOPMENT.md for the full development reference. All participants are expected to follow the Code of Conduct.

In short: branch from main, make sure npm run typecheck, npm run lint, npm test, and npm run smoke all pass, add tests for new behavior, and open a PR using the template.

To report a security vulnerability, follow SECURITY.md — do not open a public issue.

Troubleshooting

Common Issues

JSON parsing errors: All logging must go to stderr, not stdout. MCP protocol uses stdout for communication. Use the Logger class, not console.log.

Tools not appearing: Verify server configuration in Claude Desktop settings and restart Claude Desktop application.

Research features fail at call time: Set EXA_API_KEY in your environment or .env file — verify_research and perspective_shifter both require it even though all 12 tools register regardless of whether it is set.

Server not starting: Check Node.js version is 20 or higher and all dependencies are installed with npm install.

See docs/TROUBLESHOOTING.md for detailed troubleshooting guidance.

Debug Mode

Start the server with the MCP inspector:

npm run inspector

Links

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.