<p align="center"> <img src="assets/logo.png" alt="LocalData MCP Server" width="250"> </p>

LocalData MCP Server

<!-- mcp-name: io.github.chrisgve/localdata-mcp -->

LocalData MCP gives LLM agents access to local and remote data — databases, files, graphs, and structured documents — along with a full data science toolkit for analysis and modeling. It exposes 52 MCP tools across 13 database types and 20+ file formats, with memory-bounded streaming so agents can work safely on large datasets without exceeding available RAM.

Quick Start

# Install permanently
uv tool install localdata-mcp

# Or run directly without installing
uvx localdata-mcp

First-run note: Data science dependencies (scipy, scikit-learn, statsmodels, geopandas) total around 200 MB and are downloaded on first use. Subsequent starts reuse the cache. If your MCP client times out on the first launch, reconnect — the next start will be immediate.

Add to your MCP client configuration:

{
  "mcpServers": {
    "localdata": {
      "command": "localdata-mcp"
    }
  }
}

For uvx (no permanent install):

{
  "mcpServers": {
    "localdata": {
      "command": "uvx",
      "args": ["localdata-mcp"]
    }
  }
}

Then connect to any supported source and start querying:

connect_database("sales", "postgresql", "postgresql://user:pass@localhost/db")
execute_query("sales", "SELECT product, SUM(amount) FROM orders GROUP BY product")

connect_database("data", "csv", "./records.csv")
analyze_hypothesis_test("data", "SELECT amount, region FROM data", column="amount", group_column="region")

Feature Overview

Core Database (8 tools)

Connect, query, and inspect databases and files. All queries execute within configurable memory limits (default 2 GB) with automatic chunked streaming for large result sets.

Tool	Description
connect_database	Open a connection to any supported database or file
disconnect_database	Close a connection
list_databases	List active connections
execute_query	Run SQL with streaming, chunking, and preflight mode
describe_database	Show schema and table list
describe_table	Column types, indexes, row count
find_table	Locate a table across all active connections
analyze_query_preview	Estimate query cost before execution

Streaming and Memory (9 tools)

Tool	Description
next_chunk	Retrieve the next chunk of a streamed result
request_data_chunk	Fetch a specific chunk by row range
request_multiple_chunks	Batch-fetch multiple chunks in one call
manage_memory_bounds	View and configure memory limits
get_streaming_status	Check active streams and buffer usage
clear_streaming_buffer	Free memory from a specific buffer
get_query_metadata	Rich metadata for a completed query
cancel_query_operation	Cancel a running or buffered query
get_data_quality_report	Column statistics, null rates, and quality metrics

Tree / Structured Data (10 tools)

Navigate and edit TOML, JSON, and YAML files as navigable trees. Supports full CRUD with auto-creation of ancestor nodes and round-trip export to any supported format.

Tool	Description
get_node / get_children	Navigate the tree
set_node / delete_node	Create or remove nodes
get_value / set_value / delete_key	Read and write properties
list_keys	List key-value pairs at a node
move_node	Relocate a node within the tree
export_structured	Export as TOML, JSON, or YAML

Graph (14 tools)

Work with DOT, GML, GraphML, and Mermaid files as directed multigraphs. Supports full CRUD on nodes and edges, shortest-path and all-paths queries, structural statistics, and multi-format export.

Tool	Description
get_node_graph / get_neighbors / get_edges	Navigate the graph
set_node_graph / delete_node_graph	Create or remove nodes
add_edge / remove_edge	Manage edges
get_value_graph / set_value_graph / delete_key_graph / list_keys_graph	Node properties
find_path	Shortest or all paths between two nodes
get_graph_stats	Node/edge counts, density, DAG validation
export_graph	Export as DOT, GML, GraphML, or Mermaid

Search and Transform (2 tools)

Tool	Description
search_data	Regex search across query results
transform_data	Apply column transformations to result sets

Schema and Audit (3 tools)

Tool	Description
export_schema	Export full schema as JSON
get_query_log	Recent query execution history
get_error_log	Recent error log

System (2 tools)

Tool	Description
check_compatibility	Verify API backward compatibility
get_metrics	Server performance and resource metrics

Data Science (12 tools)

Run statistical analysis, modeling, and pattern detection directly on query results from any connected source.

Tool	Domain
analyze_hypothesis_test	Statistical Analysis
analyze_anova	Statistical Analysis
analyze_effect_sizes	Statistical Analysis
analyze_regression	Regression and Modeling
evaluate_model_performance	Regression and Modeling
analyze_clusters	Pattern Recognition
detect_anomalies	Pattern Recognition
reduce_dimensions	Pattern Recognition
analyze_time_series	Time Series
forecast_time_series	Time Series
analyze_rfm	Business Intelligence
analyze_ab_test	Business Intelligence

Supported Data Sources

Databases

Type	Engines
SQL	SQLite, PostgreSQL, MySQL, DuckDB
SQL (enterprise)	Oracle, MS SQL Server (pip install localdata-mcp[enterprise])
Document	MongoDB, CouchDB (pip install localdata-mcp[modern-databases])
Key-value	Redis (pip install localdata-mcp[modern-databases])
Search	Elasticsearch (pip install localdata-mcp[modern-databases])
Time series	InfluxDB (pip install localdata-mcp[modern-databases])
Graph	Neo4j (pip install localdata-mcp[modern-databases])
RDF / SPARQL	Turtle (.ttl), N-Triples (.nt), remote SPARQL endpoints

File Formats

Category	Formats
Tabular	CSV, TSV
Structured	JSON, JSONL, YAML, TOML, XML, INI
Spreadsheet	Excel (.xlsx, .xls), LibreOffice Calc (.ods), Apple Numbers (.numbers)
Analytical	Parquet, Feather, Arrow, HDF5
Graph	DOT (Graphviz), GML, GraphML, Mermaid
RDF	Turtle (.ttl), N-Triples (.nt)

Multi-sheet spreadsheets are fully supported: each sheet becomes a separately queryable table. Connect to a specific sheet with ?sheet=SheetName in the path.

Data Science Domains

Statistical Analysis — t-tests, chi-squared, Mann-Whitney, Kruskal-Wallis, and related hypothesis tests; one-way ANOVA with post-hoc tests; Cohen's d, eta-squared, and other effect size measures.

Regression and Modeling — linear, polynomial, logistic, ridge, lasso, and elastic net regression; model evaluation with R², RMSE, MAE, and classification metrics; automated feature selection.

Pattern Recognition — K-means, DBSCAN, and hierarchical clustering; anomaly detection via isolation forest, LOF, and one-class SVM; dimensionality reduction with PCA, t-SNE, and UMAP.

Time Series — decomposition, stationarity testing, autocorrelation analysis; ARIMA, SARIMA, and ETS forecasting; change point detection; multivariate analysis with VAR, Granger causality, and cointegration tests.

Business Intelligence — A/B test statistical analysis; RFM customer segmentation; cohort analysis, CLV modeling, and funnel analysis.

Geospatial — distance and coordinate calculations, spatial joins, interpolation, and network analysis.

Optimization — linear programming, constrained optimization, assignment problems, and network optimization.

Sampling and Estimation — bootstrap confidence intervals, Bayesian estimation, Monte Carlo simulation, and stratified sampling.

Architecture

• Intention-driven interface — tools accept semantic parameters ("find strong correlations") rather than requiring statistical procedure names or threshold values
• Progressive disclosure — simple calls return high-level insights with sensible defaults; advanced parameters are available when needed
• Streaming-first execution — all operations are designed for chunked processing; tools automatically switch strategies based on data size, keeping memory usage within configured bounds
• Composition metadata — every tool result includes metadata that downstream tools can use directly, enabling chained analysis without manual wiring

Configuration

LocalData MCP uses environment variables for optional settings. The defaults work for most cases.

Variable	Default	Description
LOCALDATA_MEMORY_LIMIT_MB	2048	Maximum memory per query result (MB)
LOCALDATA_MAX_CONNECTIONS	10	Maximum concurrent database connections
LOCALDATA_CHUNK_SIZE	500	Default rows per streaming chunk
LOCALDATA_BUFFER_TTL	600	Streaming buffer expiry in seconds
LOCALDATA_WORKING_DIR	process cwd	Root directory for file access (file paths are restricted to this tree)

Set in your MCP server configuration under "env", or in a .env file in the working directory.

Documentation

• Database Connection Guide — connection strings, driver setup, and security practices
• Docker Usage Guide — container deployment and configuration
• Advanced Examples — production-ready usage patterns
• Troubleshooting Guide — common issues and solutions
• FAQ — frequently asked questions
• API Reference — full tool and parameter reference

Development

git clone https://github.com/ChrisGVE/localdata-mcp.git
cd localdata-mcp
uv sync --all-extras
uv run pytest

The test suite includes 1,600+ unit tests, 234+ integration tests, and 62 enterprise-scale tests across 7 database types with 100K rows each.

Contributing

Contributions are welcome. Please read CONTRIBUTING.md before submitting a pull request.

License

Apache License 2.0 — see LICENSE for details.