Provides data analysis and SQL querying for PostgreSQL databases, with read-only access, query validation, bounded result limits, and a configurable schema search path.
Provides data analysis and SQL querying for SQLite databases, with read-only access, query validation, and bounded result limits.
Generates Typst-based PDF reports from query results, enabling formatted document creation for data analysis outputs.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-data-analysis-agent Show monthly revenue trends from the retail playground dataset"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Safe, read-only SQLite and PostgreSQL analysis for coding agents.
Tell your agent:
Please install from https://github.com/hassanvfx/mcp-data-analysis-agent.
It installs MCP Data Analysis once as a credential-free global MCP server. It does not create a database, configure a source, or change your current project.
Then open the full guide: Getting started and project configuration.
Related MCP server: sql-explorer-mcp
Once per machine | Once per data project folder |
Install the executable and machine-local absolute MCP client entries. | Configure that folder’s private |
No database URL or credential is stored globally. | The file holds exactly one SQLite path/URL or PostgreSQL URL. |
Reuse the MCP server across projects. | Run preflight, inspect schema, and analyze that project’s data. |
Move into the separate folder containing the data you want to analyze before the next step.
cd /path/to/data-projectChoose one project action:
Tell your agent | Result |
Please configure this folder. | Deterministically initializes |
Please install demo in this folder. | Initializes the hidden workspace and creates the deterministic retail demo only in this folder. |
Please enable MCP Data Analysis for this Cline project. | Activates Cline's visible runtime settings for this one project, then tells you to reload VS Code. |
The canonical commands are:
# Real source: writes an ignored, mode-0600 .mcp-data-source file.
mcp-data-cli configure-source /absolute/path/to/analytics.sqlite --yes
# Or activate the deterministic retail demo for the current folder.
mcp-data-cli demo start --yes
# Confirm a bounded, read-only connection probe succeeds.
mcp-data-cli preflightSQLite: an absolute local path or local SQLite URL.
PostgreSQL: a postgres:// or postgresql:// URL using a dedicated database-level read-only account.
Not supported: MySQL and other database dialects.
.mcp-data-source is the project’s runtime connection secret. Keep it regular (not a symlink), one line only, mode 0600, and out of version control. Global MCP configuration never contains a database URL or MCP_DATA_SOURCE_URL.
The first project-specific configure or demo action creates .mcp-data-agent/: its empty versioned workspace, schema cache, managed demo, and durable observability ledger. If you created .mcp-data-source manually, run mcp-data-cli prepare-workspace --yes before preflight. The project root must be writable; workspace_initialization_required tells you to prepare it, while PROJECT_STATE_UNAVAILABLE tells you to choose a writable regular folder. MCP never runs a query without its task and receipt evidence. Legacy root-level observability/ and .mcp-data/ locations are not read, migrated, or removed.
After preflight returns ready, ask the agent to inspect schema or run a bounded governed query. For the demo:
mcp-data-cli schema data
mcp-data-cli query data 'SELECT id, name, stock FROM products ORDER BY id' --limit 10Public install:
curl -fsSL https://raw.githubusercontent.com/hassanvfx/mcp-data-analysis-agent/main/install.sh | bashIf you already cloned this checkout:
./install.sh --localThe installer configures supported global client entries for Codex, Claude Code, Copilot, Cline, Cursor, Windsurf, and Continue. Each global entry uses the verified absolute path of this machine’s installed mcp-data-mcp, avoiding editor PATH issues while still containing no database secret. For complete client compatibility, demo cleanup, project-scoped fallback setup, Cline VS Code reload guidance, and operations details, use the hosted guide.
Cline's visible MCP settings are global to the editor host, so activate one project explicitly after opening that folder:
mcp-data-cli cline activate --project-root /absolute/path/to/data-project
mcp-data-cli cline activate --project-root /absolute/path/to/data-project --apply --yesThis writes only the absolute MCP executable, --project-root, and .mcp-data-source to every detected Cline runtime file; it never writes a database URL or environment secret. Run Developer: Reload Window in VS Code, then open Cline → MCP Servers. Switching projects requires activation again. Use mcp-data-cli cline status to inspect the exact files and their managed state. .cline/mcp.json is a legacy project-local file and is not treated as Cline's active VS Code configuration.
Tell your agent: “Please uninstall MCP Data Analysis from all agents.” It previews exact managed global entries and managed demos first. After you confirm the preview, it runs:
mcp-data-cli uninstall --all
mcp-data-cli uninstall --all --apply --yesAdd --project-root /absolute/path/to/project for every project-scoped fallback entry you also want removed. Cleanup never deletes custom sources, databases, governance files, observability evidence, or unrelated client settings.
Inputs: title (required), objective (required). Returns: object
Inputs: task_id (required). Returns: object
Inputs: sql (required), source_alias (required), current_parameters_json (required), previous_parameters_json (required), +1 more. Returns: object
Inputs: task_id (required), findings (required), next_steps. Returns: object
Create a seeded retail demo only after the user explicitly approves it.
Inputs: confirmed. Returns: object
Inputs: sql (required), source_alias (required), current_parameters_json (required), baseline_parameters_json (required), +1 more. Returns: object
Inputs: task_id (required). Returns: object
Inputs: sql (required), source_alias (required), parameters_json. Returns: object
Inputs: source_alias (required). Returns: result
Inputs: no named parameters. Returns: result
Inputs: no named parameters. Returns: result
Validate this project's source configuration without revealing its URL.
Inputs: no named parameters. Returns: object
Inputs: name (required), task_id. Returns: object
Inputs: source_alias (required). Returns: object
Inputs: source_alias (required). Returns: result
Inputs: task_id (required). Returns: result
Inputs: sql (required), source_alias (required), limit, offset, +2 more. Returns: object
Inputs: sql (required), source_alias (required), parameters_json. Returns: object
Inputs: no named parameters. Returns: object
Explain the project source-file configuration requirement.
Inputs: no named parameters. Returns: object
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 20 tools
Many tools have clear distinct responsibilities, but some pairs overlap in intent (get_schema vs schema_state, validate_sql vs validate_and_execute) and task lifecycle tools (begin/complete/evaluate) could be confused without descriptions. The boundaries are mostly inferable but not consistently crisp.
Tool names mix verb-first actions (verify_observability, get_schema, run_metric) with noun-only states (schema_state, task_timeline) and one-word commands (preflight, welcome). The pattern is inconsistent, though most names are readable.
20 tools is on the higher side but not unreasonable for a data-analysis agent covering tasks, schema, SQL, metrics, and recipes. Several tools could be consolidated (e.g., schema_state into get_schema, validate_and_execute with validate_sql), which would make the count more focused.
The set covers core data-analysis workflows: task lifecycle, schema introspection, SQL validation, metric execution, comparisons, and change detection. It lacks explicit data fetching/result retrieval endpoints, but the metric and SQL tools likely fill that role; onboarding tools are extra rather than essential.
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