LocalData MCP Tools Reference

Complete reference documentation for all 52 MCP tools. Organized by category for quick navigation.

Table of Contents

  1. Core Database (8 tools)

  2. Streaming & Memory (9 tools)

  3. Tree/Structured Data (10 tools)

  4. Graph Operations (7 tools)

  5. Search & Transform (2 tools)

  6. Schema & Audit (3 tools)

  7. System (1 tool)

  8. Data Science (12 tools)

Core Database (8 tools)

connect_database

Open a connection to a database.

Parameters:

Name

Type

Required

Description

name

string

Yes

Unique connection identifier (e.g., “analytics_db”, “user_data”)

db_type

string

Yes

Database type: sqlite, postgresql, mysql, duckdb, csv, json, yaml, toml, excel, ods, numbers, xml, ini, tsv, parquet, feather, arrow, hdf5, dot, gml, graphml, mermaid, turtle, ntriples, sparql

conn_string

string

Yes

Connection string or file path

sheet_name

string

No

Sheet name for Excel/ODS/Numbers or dataset name for HDF5

auth

string

No

JSON authentication config (e.g., {"method": "wallet", "wallet_path": "/path"})

Returns: Connection summary with metadata (JSON)

Example:

# SQL database
connect_database("mydb", "postgresql", "postgresql://user:pass@localhost/dbname")

# CSV file
connect_database("data", "csv", "/path/to/file.csv")

# Graph file
connect_database("network", "graphml", "/path/to/network.graphml")

Composition hints: Use with execute_query, describe_database, or data manipulation tools.

disconnect_database

Close a connection to a database. All connections close automatically on script termination.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name to close

Returns: Success/error message with cleanup details (JSON)

Example:

disconnect_database("mydb")

Composition hints: Call after completing work with a database to free resources.

execute_query

Execute a SQL query and return results as JSON with memory-aware streaming.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

query

string

Yes

SQL query to execute

chunk_size

integer

No

Results per chunk for pagination

enable_analysis

boolean

No

Perform pre-query analysis (default: true)

include_blobs

boolean

No

Base64-encode BLOBs in results (default: false)

preflight

boolean

No

Run EXPLAIN only (default: false)

Returns: Query results as JSON or streaming metadata (JSON)

Example:

execute_query("mydb", "SELECT * FROM users WHERE active = true", chunk_size=100)

Composition hints: Use with next_chunk for large result sets, get_query_metadata for analysis.

analyze_query_preview

Analyze a query without executing it to preview resource requirements.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

query

string

Yes

SQL query to analyze

Returns: Analysis including estimated rows, memory, execution time, and risks (JSON)

Example:

analyze_query_preview("mydb", "SELECT * FROM large_table JOIN other_table ON ...")

Composition hints: Use before execute_query on complex queries to assess feasibility.

list_databases

List all available database connections with their SQL flavor information.

Parameters:

Name

Type

Required

Description

include_staging

boolean

No

Include active staging databases (default: false)

Returns: Array of connection objects with names and types (JSON)

Example:

list_databases()

Composition hints: Use to discover available connections for workflow orchestration.

describe_database

Get detailed information about a database including its schema in JSON format.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

Returns: Database schema with tables, columns, types, and relationships (JSON)

Example:

describe_database("mydb")

Composition hints: Use with find_table to locate specific tables, or describe_table for details.

find_table

Find which database contains a specific table by name.

Parameters:

Name

Type

Required

Description

table_name

string

Yes

Name of table to locate

Returns: Connection name containing the table, or error if not found (JSON)

Example:

find_table("users")

Composition hints: Use in multi-database workflows to locate data without knowing connection.

describe_table

Get detailed schema information for a specific table.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

table_name

string

Yes

Name of table to describe

Returns: Table schema with columns, types, constraints, and indexes (JSON)

Example:

describe_table("mydb", "users")

Composition hints: Use before writing queries to understand table structure and column types.

Streaming & Memory (9 tools)

next_chunk

Retrieve the next chunk of rows from a buffered query result.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of buffered query result

start_row

integer

Yes

Starting row number (1-based)

chunk_size

string

Yes

Number of rows or “all” for remaining

Returns: Chunk of rows with metadata (JSON)

Example:

next_chunk("mydb_12345_abc1", 1, "100")

Composition hints: Chain multiple calls to paginate through large result sets.

manage_memory_bounds

Monitor and manage memory usage across all streaming operations.

Parameters: None

Returns: Memory status, usage statistics, and cleanup actions taken (JSON)

Example:

manage_memory_bounds()

Composition hints: Call when memory warnings appear or before large operations.

get_streaming_status

Get detailed status of all active streaming operations and memory usage.

Parameters: None

Returns: Active buffers, memory usage, performance metrics (JSON)

Example:

get_streaming_status()

Composition hints: Monitor streaming workloads and identify bottlenecks.

clear_streaming_buffer

Clear a specific streaming result buffer to free memory immediately.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of buffer to clear

Returns: Confirmation message (JSON)

Example:

clear_streaming_buffer("mydb_12345_abc1")

Composition hints: Use after finishing with a large result set.

get_query_metadata

Get comprehensive metadata for a query result including quality metrics and processing recommendations.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of query result

Returns: LLM-friendly summary, quality metrics, complexity analysis (JSON)

Example:

get_query_metadata("mydb_12345_abc1")

Composition hints: Use with LLM-based analysis workflows for decision making.

request_data_chunk

Retrieve a specific chunk of data using the LLM communication protocol.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of query result

chunk_id

integer

Yes

Chunk ID to retrieve (0-based)

Returns: Chunk data with metadata (JSON)

Example:

request_data_chunk("mydb_12345_abc1", 0)

Composition hints: Use for targeted chunk retrieval in progressive loading workflows.

request_multiple_chunks

Retrieve multiple chunks efficiently in a single call.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of query result

chunk_ids

string

Yes

Comma-separated chunk IDs (e.g., “0,1,2”)

Returns: Multiple chunks with metadata (JSON)

Example:

request_multiple_chunks("mydb_12345_abc1", "0,1,2,3,4")

Composition hints: Use to load specific chunks in parallel.

cancel_query_operation

Cancel an ongoing query operation and free resources.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of query to cancel

reason

string

No

Reason for cancellation (default: “User requested”)

Returns: Cancellation status message (JSON)

Example:

cancel_query_operation("mydb_12345_abc1", "User interrupt")

Composition hints: Use on long-running queries to stop execution.

get_data_quality_report

Get comprehensive data quality assessment for a query result.

Parameters:

Name

Type

Required

Description

query_id

string

Yes

ID of query to assess

Returns: Detailed quality report including nulls, duplicates, outliers (JSON)

Example:

get_data_quality_report("mydb_12345_abc1")

Composition hints: Use before analytics to understand data cleanliness.

Tree/Structured Data (10 tools)

get_node

Get node details or summary for tree, graph, or RDF connections.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

No

Node path (tree), node_id (graph), or subject URI (RDF)

Returns: Node details or root summary (JSON)

Example:

get_node("config", "server/host")
get_node("network", "node-123")

Composition hints: Use with get_children to traverse hierarchies.

get_children

Get children of a node with pagination.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

No

Parent node path (null for root)

offset

integer

No

Pagination offset (default: 0)

limit

integer

No

Rows per page (default: 50)

Returns: Array of child nodes with metadata (JSON)

Example:

get_children("config", "database", offset=0, limit=20)

Composition hints: Chain calls with different offsets to paginate through large hierarchies.

set_node

Create a node in tree or graph connection.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Path for new node

label

string

No

Display label (graph only)

Returns: Confirmation with node details (JSON)

Example:

set_node("config", "app/features/new_feature", "New Feature")

Composition hints: Use with set_value to add properties to nodes.

move_node

Move a node and its subtree under a new parent or to root.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Node path to move

new_parent

string

No

New parent path (null for root)

Returns: Confirmation with new location (JSON)

Example:

move_node("config", "old/location/node", "new/location")

Composition hints: Use for reorganizing hierarchical structures.

delete_node

Delete a node and all its descendants.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Node path to delete

Returns: Confirmation with deletion details (JSON)

Example:

delete_node("config", "deprecated/feature")

Composition hints: Use carefully as deletion cascades to all children.

list_keys

List key-value pairs at a node with pagination.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Node path

offset

integer

No

Pagination offset (default: 0)

limit

integer

No

Results per page (default: 50)

Returns: Array of key-value pairs (JSON)

Example:

list_keys("config", "database")

Composition hints: Use with get_value to inspect node properties.

get_value

Get a specific property value from a node.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Node path

key

string

Yes

Property key to retrieve

Returns: Property value and metadata (JSON)

Example:

get_value("config", "database", "host")

Composition hints: Use to read individual node properties.

set_value

Set a property on a node (auto-creates node if needed).

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Node path

key

string

Yes

Property key

value

string

Yes

Property value

value_type

string

No

Data type hint

Returns: Confirmation with updated node (JSON)

Example:

set_value("config", "database", "host", "localhost", "string")

Composition hints: Use to modify node properties.

delete_key

Delete a property from a node.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

path

string

Yes

Node path

key

string

Yes

Property key to delete

Returns: Confirmation with remaining properties (JSON)

Example:

delete_key("config", "database", "deprecated_setting")

Composition hints: Use to remove obsolete node properties.

export_structured

Export tree or RDF data as TOML, JSON, YAML, Turtle, or N-Triples.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

format

string

Yes

Export format: json, yaml, toml, turtle, ntriples

path

string

No

Root path to export (null for all)

Returns: Exported data in requested format (string)

Example:

export_structured("config", "yaml", "server")

Composition hints: Use for data portability and backup.

Graph Operations (7 tools)

get_neighbors

Get neighbors of a graph node with edge information.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

node_id

string

Yes

Node ID

direction

string

No

Direction: “in”, “out”, or “both” (default: both)

offset

integer

No

Pagination offset (default: 0)

limit

integer

No

Results per page (default: 50)

Returns: Array of neighbors with edge information (JSON)

Example:

get_neighbors("network", "user-123", direction="out")

Composition hints: Use for network analysis and traversal.

get_edges

List edges in a graph, optionally filtered by node.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

node_id

string

No

Filter by node (null for all edges)

offset

integer

No

Pagination offset (default: 0)

limit

integer

No

Results per page (default: 50)

Returns: Array of edges with source, target, and properties (JSON)

Example:

get_edges("network", node_id="user-123")

Composition hints: Use for graph structure analysis and export.

add_edge

Add an edge to a graph (auto-creates nodes if needed).

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

source

string

Yes

Source node ID

target

string

Yes

Target node ID

label

string

No

Edge label or relationship type

weight

number

No

Edge weight (for weighted graphs)

Returns: Confirmation with edge details (JSON)

Example:

add_edge("network", "user-1", "user-2", "follows", weight=1.0)

Composition hints: Use to build or modify graphs programmatically.

remove_edge

Remove an edge from a graph.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

source

string

Yes

Source node ID

target

string

Yes

Target node ID

label

string

No

Edge label (for filtering)

Returns: Confirmation of removal (JSON)

Example:

remove_edge("network", "user-1", "user-2", "follows")

Composition hints: Use to modify graph topology.

find_path

Find path(s) between two graph nodes.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

source

string

Yes

Start node ID

target

string

Yes

End node ID

algorithm

string

No

Algorithm: “shortest” (default) or “all”

Returns: Path(s) with nodes and edges (JSON)

Example:

find_path("network", "user-1", "user-5", algorithm="shortest")

Composition hints: Use for network analysis and influence tracing.

get_graph_stats

Get advanced graph statistics including centrality measures.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

Returns: Graph metrics: node count, edge count, density, diameter, clustering (JSON)

Example:

get_graph_stats("network")

Composition hints: Use to characterize network properties.

export_graph

Export graph as DOT, GML, or GraphML format.

Parameters:

Name

Type

Required

Description

name

string

Yes

Connection name

format

string

Yes

Format: dot, gml, graphml

node_id

string

No

Export subgraph from node (null for all)

Returns: Graph in requested format (string)

Example:

export_graph("network", "graphml")

Composition hints: Use for graph visualization and portability.

Search & Transform (2 tools)

search_data

Search query results for regex pattern matches.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

query

string

Yes

SQL query to search within

pattern

string

Yes

Regex pattern to find

columns

string

No

Comma-separated column names (null for all)

case_sensitive

boolean

No

Case-sensitive search (default: true)

max_matches

integer

No

Maximum matches to return (default: 100)

Returns: Matching rows with metadata (JSON)

Example:

search_data("mydb", "SELECT * FROM emails", ".*@company\\.com", columns="email", case_sensitive=False)

Composition hints: Use for content discovery and data validation.

transform_data

Apply regex find/replace to a column in query results.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

query

string

Yes

SQL query to transform

column

string

Yes

Column name to apply transformation

find

string

Yes

Regex pattern to find

replace

string

Yes

Replacement string (supports capture groups)

max_rows

integer

No

Maximum rows to process (default: 1000)

Returns: Transformed data preview (JSON)

Example:

transform_data("mydb", "SELECT * FROM logs", "message", "ERROR: (.*)", "CRITICAL: $1")

Composition hints: Use for data cleaning and standardization.

Schema & Audit (3 tools)

export_schema

Export database schema in various formats.

Parameters:

Name

Type

Required

Description

name

string

Yes

Database connection name

format

string

No

Format: json_schema, python, typescript, sql_ddl (default: json_schema)

tables

string

No

Comma-separated table names (null for all)

Returns: Schema in requested format (string/JSON)

Example:

export_schema("mydb", format="typescript", tables="users,posts")

Composition hints: Use for code generation and documentation.

get_query_log

Get recent query execution history.

Parameters:

Name

Type

Required

Description

database

string

No

Filter by database (null for all)

status

string

No

Filter by status: success, error, timeout

since_minutes

integer

No

Look back this many minutes (default: 60)

limit

integer

No

Maximum entries to return (default: 50)

Returns: Query history entries with execution details (JSON)

Example:

get_query_log(database="mydb", status="error", since_minutes=30)

Composition hints: Use for debugging and performance analysis.

get_error_log

Get recent error and timeout history.

Parameters:

Name

Type

Required

Description

database

string

No

Filter by database (null for all)

since_minutes

integer

No

Look back this many minutes (default: 60)

limit

integer

No

Maximum entries to return (default: 50)

Returns: Error entries with context and suggestions (JSON)

Example:

get_error_log(database="mydb", since_minutes=60)

Composition hints: Use for troubleshooting connection and query issues.

System (1 tool)

check_compatibility

Check backward compatibility status and get migration recommendations.

Parameters:

Name

Type

Required

Description

generate_migration_script

boolean

No

Generate migration script for legacy config (default: false)

Returns: Compatibility report and migration guidance (JSON)

Example:

check_compatibility(generate_migration_script=True)

Composition hints: Use during upgrades or configuration migrations.

Data Science (12 tools)

analyze_hypothesis_test

Run statistical hypothesis test on query results.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query returning data

test_type

string

Yes

Test type: t_test, chi_square, mann_whitney, wilcoxon, kruskal, fisher

column

string

Yes

Column to test

group_column

string

No

Column defining groups for comparison

alpha

number

No

Significance level (default: 0.05)

alternative

string

No

Two-sided, less, or greater (default: two-sided)

Returns: Test statistics, p-value, and interpretation (JSON)

Example:

analyze_hypothesis_test("mydb", "SELECT * FROM experiments", "t_test", "score", "treatment", alpha=0.05)

Composition hints: Use for A/B testing and experiment validation.

analyze_anova

Analyze variance across multiple groups using ANOVA.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with group and value columns

value_column

string

Yes

Column with numeric values

group_column

string

Yes

Column defining groups

Returns: F-statistic, p-value, group means, effect size (JSON)

Example:

analyze_anova("mydb", "SELECT * FROM sales", "revenue", "region")

Composition hints: Use for comparing means across multiple groups.

analyze_effect_sizes

Calculate effect sizes for statistical tests.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query returning data

effect_type

string

Yes

Type: cohens_d, cramers_v, eta_squared

column1

string

Yes

First column/group

column2

string

No

Second column for comparison

Returns: Effect size value and interpretation (JSON)

Example:

analyze_effect_sizes("mydb", "SELECT * FROM experiments", "cohens_d", "control", "treatment")

Composition hints: Pair with hypothesis tests for practical significance.

analyze_regression

Fit regression models to data.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with feature and target columns

target_column

string

Yes

Column to predict

feature_columns

string

Yes

Comma-separated feature columns

model_type

string

No

linear, ridge, lasso, polynomial (default: linear)

Returns: Model coefficients, R², predictions (JSON)

Example:

analyze_regression("mydb", "SELECT * FROM properties", "price", "sqft,bedrooms,year_built", model_type="linear")

Composition hints: Use with evaluate_model_performance for validation.

evaluate_model_performance

Evaluate trained model performance on test data.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with actual and predicted columns

actual_column

string

Yes

Column with true values

predicted_column

string

Yes

Column with predictions

metric_type

string

No

r2, rmse, mae, accuracy (default: r2)

Returns: Performance metrics and diagnostics (JSON)

Example:

evaluate_model_performance("mydb", "SELECT * FROM test_results", "actual_price", "predicted_price", metric_type="rmse")

Composition hints: Use after regression or classification.

analyze_clusters

Perform clustering analysis on query data.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with feature columns

feature_columns

string

Yes

Comma-separated numeric columns

n_clusters

integer

No

Number of clusters (default: auto)

algorithm

string

No

kmeans, hierarchical, dbscan (default: kmeans)

Returns: Cluster assignments, centroids, silhouette score (JSON)

Example:

analyze_clusters("mydb", "SELECT * FROM customers", "spending,frequency,recency", n_clusters=5, algorithm="kmeans")

Composition hints: Use for customer segmentation and pattern discovery.

detect_anomalies

Detect anomalies in query data using statistical methods.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with numeric columns

column

string

Yes

Column to analyze for anomalies

threshold

number

No

Standard deviation threshold (default: 3.0)

method

string

No

zscore, iqr, isolation_forest (default: zscore)

Returns: Anomaly flags, scores, flagged rows (JSON)

Example:

detect_anomalies("mydb", "SELECT * FROM transactions", "amount", threshold=2.5, method="iqr")

Composition hints: Use for data quality and fraud detection.

reduce_dimensions

Perform dimensionality reduction on high-dimensional data.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with feature columns

feature_columns

string

Yes

Comma-separated numeric columns

n_components

integer

No

Target number of dimensions (default: 2)

method

string

No

pca, tsne, umap (default: pca)

Returns: Reduced components, explained variance (JSON)

Example:

reduce_dimensions("mydb", "SELECT * FROM gene_data", "gene_*", n_components=3, method="pca")

Composition hints: Use before clustering on high-dimensional data.

analyze_time_series

Analyze time series data for trends and patterns.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with timestamp and value columns

time_column

string

Yes

Timestamp column name

value_column

string

Yes

Numeric value column

period

string

No

Decomposition period: daily, weekly, monthly

Returns: Trend, seasonal, residual components; stationarity test (JSON)

Example:

analyze_time_series("mydb", "SELECT * FROM stock_prices", "date", "price", period="daily")

Composition hints: Use before forecast_time_series.

forecast_time_series

Generate time series forecasts.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with historical data

time_column

string

Yes

Timestamp column name

value_column

string

Yes

Numeric value column

periods_ahead

integer

No

Number of periods to forecast (default: 10)

method

string

No

arima, exponential_smoothing, prophet (default: arima)

Returns: Forecast values, confidence intervals (JSON)

Example:

forecast_time_series("mydb", "SELECT * FROM sales_history", "date", "sales", periods_ahead=30, method="arima")

Composition hints: Use for sales, demand, and resource planning.

analyze_rfm

Perform RFM (Recency, Frequency, Monetary) customer analysis.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with customer transactions

customer_id

string

Yes

Column with customer identifiers

date_column

string

Yes

Transaction date column

amount_column

string

Yes

Transaction amount column

Returns: RFM scores, customer segments, value tiers (JSON)

Example:

analyze_rfm("mydb", "SELECT * FROM transactions", "customer_id", "order_date", "order_value")

Composition hints: Use for customer segmentation and targeting.

analyze_ab_test

Analyze results from A/B tests with statistical rigor.

Parameters:

Name

Type

Required

Description

connection_name

string

Yes

Database connection name

query

string

Yes

SQL query with control/test and outcome

group_column

string

Yes

Column denoting control/treatment groups

outcome_column

string

Yes

Column with binary or continuous outcome

confidence_level

number

No

Confidence level (default: 0.95)

Returns: Test statistics, p-value, sample size, power analysis (JSON)

Example:

analyze_ab_test("mydb", "SELECT * FROM experiment_results", "group", "converted", confidence_level=0.95)

Composition hints: Use for experimentation and decision-making.

Quick Reference Summary

Category

Count

Purpose

Core Database

8

Connect, query, inspect databases

Streaming & Memory

9

Handle large results, memory management

Tree/Structured

10

Hierarchical JSON, YAML, TOML data

Graph

7

Network analysis and manipulation

Search & Transform

2

Pattern matching and text replacement

Schema & Audit

3

Introspection and query history

System

1

Compatibility and migration

Data Science

12

Statistical analysis and forecasting

Total

52

Complete LLM-native data platform

Parameter Type Reference

Type

Format

Example

string

UTF-8 text

“mydb”, “users”, “/path/to/file”

integer

Whole numbers

100, -1, 0

number

Float/decimal

0.05, 3.14, 2.5

boolean

true/false

true, false

optional

[ ] indicates optional

Parameter with [No] in Required column

Return Format Convention

All tools return JSON-formatted responses with:

{
  "status": "success|error|warning",
  "data": { },
  "metadata": { }
}

Successful queries return "status": "success". Errors include context in metadata for debugging.

Composition Patterns

Sequential Loading

execute_query() -> get_query_metadata() -> request_multiple_chunks()

Exploration Workflow

list_databases() -> describe_database() -> find_table() -> describe_table()

Data Transformation

execute_query() -> search_data() -> transform_data() -> export_schema()

Analysis Pipeline

execute_query() -> analyze_clusters() -> reduce_dimensions() -> get_graph_stats()

For integration examples and advanced workflows, see the main documentation.