Codelogic

lineai-mcp-server

An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.

Documentation

Code-derived architecture documentation lives in docs/ (added in LIN-737; updated for the LIN-699 changes — PR #61 / neo4cape #1860):

• docs/architecture.md — full architecture analysis: module map, tool surface and the Lineai /api/... endpoints each tool calls, auth flow, caching, error-handling taxonomy, configuration, packaging/release, and findings.
• docs/architecture-diagrams.md — Mermaid diagram set (ecosystem, module map, call sequence, auth/caching, error taxonomy).
• docs/rendered-diagrams/ — committed SVG renders of every diagram.

Components

Tools

The server implements eight tools: two impact tools plus six graph tools backed by the Lineai graph HTTP API.

Code Analysis Tools

• lineai-method-impact: Pulls an impact assessment from the Lineai server's APIs for your code.
  • Takes the given "method" that you're working on; "class" is optional (a case-insensitive filter used when several matches exist).
• lineai-database-impact: Analyzes impacts between code and database entities.
  • Takes the database entity type (column, table, or view) and its name.

All eight tools also accept optional workspace and materialized_view_id arguments to scope a single call; when neither is given (and LINEAI_WORKSPACE_NAME is unset) the server's default workspace's latest materialized view is used automatically.

Graph API tools

These call POST / GET endpoints under /api/ai-retrieval/graph/ on the same host as LINEAI_SERVER_HOST, using the same session auth as other MCP tools. If graph routes are not deployed, the server returns a clear “graph not available” style message (often after HTTP 404).

• lineai-graph-capabilities: GET — discover supported relationship types, limits, and flags for the workspace materialized view (materializedViewId resolved like every other tool: explicit id → workspace argument → LINEAI_WORKSPACE_NAME → server default).
• lineai-graph-search: Search nodes by text query / q and/or identity_prefix; optional scan_space, limit, etc.
• lineai-graph-impact: Dependency / blast-radius style traversal from seed_node_ids.
• lineai-graph-path-explain: Shortest-path style explanation between from_node_id and to_node_id.
• lineai-graph-validate-change-scope: Heuristic checklist / risk summary for a proposed change given seed nodes and proposed_change_summary.
• lineai-graph-owners: Resolve a node by node_id or identity_prefix and surface property fields whose names contain "owner".

Tool arguments accept snake_case aliases (for example materialized_view_id, seed_node_ids) where noted in the MCP schema; request bodies sent to Lineai use camelCase JSON keys.

Install

Pre Requisites

The MCP server relies upon Astral UV to run, please install

MacOS Workaround for uvx

There is a known issue with uvx on MacOS where the Lineai MCP server may fail to launch in certain IDEs (such as Cursor), resulting in errors like: See issue #11

Failed to connect client closed

This appears to be a problem with Astral uvx running on MacOS. The following can be used as a workaround:

1. Clone this project locally.
2. Configure your mcp.json to use uv instead of uvx. For example:

{
  "mcpServers": {
    "lineai-mcp-server": {
      "type": "stdio",
      "command": "<PATH_TO_UV>/uv",
      "args": [
        "--directory",
        "<PATH_TO_THIS_REPO>/lineai-mcp-server-main",
        "run",
        "lineai-mcp-server"
      ],
      "env": {
        "LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
        "LINEAI_USERNAME": "<my username>",
        "LINEAI_PASSWORD": "<my password>",
        "LINEAI_WORKSPACE_NAME": "<my workspace>",
        "LINEAI_DEBUG_MODE": "true"
      }
    }
  }
}

3. Restart Cursor.
4. Ensure the Cursor Global Rule for Lineai is in place.
5. Open the MCP tab in Cursor and refresh the lineai-mcp-server.
6. Ask Cursor to make a code change in an existing class. The MCP server should now run the impact analysis successfully.

Configuration for Different IDEs

Visual Studio Code Configuration

To configure this MCP server in VS Code:

1. First, ensure you have GitHub Copilot agent mode enabled in VS Code.

2. Create a .vscode/mcp.json file in your workspace with the following configuration:

{
  "servers": {
    "lineai-mcp-server": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "lineai-mcp-server@latest"
      ],
      "env": {
        "LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
        "LINEAI_USERNAME": "<my username>",
        "LINEAI_PASSWORD": "<my password>",
        "LINEAI_WORKSPACE_NAME": "<my workspace>",
        "LINEAI_DEBUG_MODE": "true"
      }
    }
  }
}

Note: On some systems, you may need to use the full path to the uvx executable instead of just "uvx". For example: /home/user/.local/bin/uvx on Linux/Mac or C:\Users\username\AppData\Local\astral\uvx.exe on Windows.

3. Alternatively, you can run the MCP: Add Server command from the Command Palette and provide the server information.

4. To manage your MCP servers, use the MCP: List Servers command from the Command Palette.

5. Once configured, the server's tools will be available to Copilot agent mode. You can toggle specific tools on/off as needed by clicking the Tools button in the Chat view when in agent mode.

6. To use the Lineai tools in agent mode, you can specifically ask about code impacts or database relationships, and the agent will utilize the appropriate tools.

Claude Desktop Configuration

Configure Claude Desktop by editing the configuration file:

• On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
• On Windows: %APPDATA%/Claude/claude_desktop_config.json
• On Linux: ~/.config/Claude/claude_desktop_config.json

Add the following to your configuration file:

"mcpServers": {
  "lineai-mcp-server": {
    "command": "uvx",
    "args": [
      "lineai-mcp-server@latest"
    ],
    "env": {
      "LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
      "LINEAI_USERNAME": "<my username>",
      "LINEAI_PASSWORD": "<my password>",
      "LINEAI_WORKSPACE_NAME": "<my workspace>"
    }
  }
}

Note: On some systems, you may need to use the full path to the uvx executable instead of just "uvx". For example: /home/user/.local/bin/uvx on Linux/Mac or C:\Users\username\AppData\Local\astral\uvx.exe on Windows.

After adding the configuration, restart Claude Desktop to apply the changes.

Windsurf IDE Configuration

To run this MCP server with Windsurf IDE:

Configure Windsurf IDE:

To configure Windsurf IDE, you need to create or modify the ~/.codeium/windsurf/mcp_config.json configuration file.

Add the following configuration to your file:

"mcpServers": {
  "lineai-mcp-server": {
    "command": "uvx",
    "args": [
      "lineai-mcp-server@latest"
    ],
    "env": {
      "LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
      "LINEAI_USERNAME": "<my username>",
      "LINEAI_PASSWORD": "<my password>",
      "LINEAI_WORKSPACE_NAME": "<my workspace>"
    }
  }
}

Note: On some systems, you may need to use the full path to the uvx executable instead of just "uvx". For example: /home/user/.local/bin/uvx on Linux/Mac or C:\Users\username\AppData\Local\astral\uvx.exe on Windows.

After adding the configuration, restart Windsurf IDE or refresh the tools to apply the changes.

Cursor Configuration

To configure the Lineai MCP server in Cursor:

1. Configure the MCP server by creating a .cursor/mcp.json file:

{
  "mcpServers": {
    "lineai-mcp-server": {
      "command": "uvx",
      "args": [
        "lineai-mcp-server@latest"
      ],
      "env": {
        "LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
        "LINEAI_USERNAME": "<my username>",
        "LINEAI_PASSWORD": "<my password>",
        "LINEAI_WORKSPACE_NAME": "<my workspace>",
        "LINEAI_DEBUG_MODE": "true"
      }
    }
  }
}

Note: On some systems, you may need to use the full path to the uvx executable instead of just "uvx". For example: /home/user/.local/bin/uvx on Linux/Mac or C:\Users\username\AppData\Local\astral\uvx.exe on Windows.

2. Restart Cursor to apply the changes.

The Lineai MCP server tools will now be available in your Cursor workspace.

AI Assistant Instructions/Rules

To help the AI assistant use the Lineai tools effectively, you can add the following instructions/rules to your client's configuration. We recommend customizing these instructions to align with your team's specific coding standards, best practices, and workflow requirements:

When the graph API is available on your Lineai host, extend your rules with the same guidance the server already advertises in its MCP instructions: use lineai-graph-* tools (search, impact, path-explain, validate-change-scope, owners, capabilities) for bounded graph discovery; if graph calls fail with “not available”, fall back to lineai-method-impact / lineai-database-impact.

VS Code (GitHub Copilot) Instructions

Create a .vscode/copilot-instructions.md file with the following content:

# Lineai MCP Server Instructions

When modifying existing code methods:
- Use lineai-method-impact to analyze code changes
- Use lineai-database-impact for database modifications
- When the Lineai graph API is available, use lineai-graph-* tools (search, impact, path-explain, validate-change-scope, owners, capabilities) for bounded graph discovery; otherwise rely on method/database impact tools
- Highlight impact results for the modified methods

When modifying SQL code or database entities:
- Always use lineai-database-impact to analyze potential impacts
- Highlight impact results for the modified database entities

To use the Lineai tools effectively:
- For code impacts: Ask about specific methods or functions
- For database relationships: Ask about tables, views, or columns
- For graph discovery: Prefer lineai-graph-* tools when available
- Review the impact results before making changes
- Consider both direct and indirect impacts

Claude Desktop Instructions

Create a file ~/.claude/instructions.md with the following content:

# Lineai MCP Server Instructions

When modifying existing code methods:
- Use lineai-method-impact to analyze code changes
- Use lineai-database-impact for database modifications
- When the Lineai graph API is available, use lineai-graph-* tools (search, impact, path-explain, validate-change-scope, owners, capabilities) for bounded graph discovery; otherwise rely on method/database impact tools
- Highlight impact results for the modified methods

When modifying SQL code or database entities:
- Always use lineai-database-impact to analyze potential impacts
- Highlight impact results for the modified database entities

To use the Lineai tools effectively:
- For code impacts: Ask about specific methods or functions
- For database relationships: Ask about tables, views, or columns
- For graph discovery: Prefer lineai-graph-* tools when available
- Review the impact results before making changes
- Consider both direct and indirect impacts

Windsurf IDE Rules

Create or modify the ~/.codeium/windsurf/memories/global_rules.md markdown file with the following content:

When modifying existing code methods:
- Use lineai-method-impact to analyze code changes
- Use lineai-database-impact for database modifications
- When the Lineai graph API is available, use lineai-graph-* tools (search, impact, path-explain, validate-change-scope, owners, capabilities) for bounded graph discovery; otherwise rely on method/database impact tools
- Highlight impact results for the modified methods

When modifying SQL code or database entities:
- Always use lineai-database-impact to analyze potential impacts
- Highlight impact results for the modified database entities

To use the Lineai tools effectively:
- For code impacts: Ask about specific methods or functions
- For database relationships: Ask about tables, views, or columns
- For graph discovery: Prefer lineai-graph-* tools when available
- Review the impact results before making changes
- Consider both direct and indirect impacts

Cursor Global Rule

To configure Lineai rules in Cursor:

1. Open Cursor Settings
2. Navigate to the "Rules" section
3. Add the following content to "User Rules":

# Lineai MCP Server Rules
## Codebase
- The Lineai MCP Server is for java, javascript, typescript, and C# dotnet codebases
- don't run the tools on python or other non supported codebases
## AI Assistant Behavior
- When modifying existing code methods:
  - Use lineai-method-impact to analyze code changes
  - Use lineai-database-impact for database modifications
  - When the Lineai graph API is available, use lineai-graph-* tools (search, impact, path-explain, validate-change-scope, owners, capabilities) for bounded graph discovery; otherwise rely on method/database impact tools
  - Highlight impact results for the modified methods
- When modifying SQL code or database entities:
  - Always use lineai-database-impact to analyze potential impacts
  - Highlight impact results for the modified database entities
- To use the Lineai tools effectively:
  - For code impacts: Ask about specific methods or functions
  - For database relationships: Ask about tables, views, or columns
  - Review the impact results before making changes
  - Consider both direct and indirect impacts

Environment Variables

The following environment variables can be configured to customize the behavior of the server:

• LINEAI_SERVER_HOST: The URL of the Lineai server (required). All API paths (/api/authenticate, /api/ai-retrieval/..., /api/dependency/..., /api/materialized-view...) are appended to it.
• LINEAI_USERNAME: Your Lineai username (required).
• LINEAI_PASSWORD: Your Lineai password (required).
• LINEAI_WORKSPACE_NAME: The default workspace to use. Optional: individual tool calls can override it with workspace / materialized_view_id arguments, and when nothing is specified at all the server's default workspace's latest materialized view is used.
• LINEAI_DEBUG_MODE: Set to true to enable debug mode. When enabled, additional debug files such as timing_log.txt and impact_data*.json will be generated. Defaults to false.

Tuning (optional — read once at server startup)

• LINEAI_TOKEN_CACHE_TTL: Authentication-token cache lifetime in seconds. Defaults to 3600.
• LINEAI_METHOD_CACHE_TTL: Method shortname-search result cache lifetime in seconds. Defaults to 300.
• LINEAI_IMPACT_CACHE_TTL: Impact-analysis result cache lifetime in seconds. Defaults to 300.
• LINEAI_MV_CACHE_TTL: Materialized-view resolution cache lifetime in seconds (workspace name / server default → view id). Defaults to 300.
• LINEAI_REQUEST_TIMEOUT: Overall HTTP request timeout in seconds for all Lineai API calls. Defaults to 120.0.
• LINEAI_CONNECT_TIMEOUT: HTTP connect timeout in seconds. Defaults to 30.0.

Tests only

• LINEAI_TEST_MODE: When set (any value), the server skips loading a .env file so tests fully control the environment.
• LINEAI_GRAPH_E2E_REQUIRED: Set to 1 when running graph MCP integration tests if you want missing graph APIs (HTTP 404 / “Graph API not available”) to fail the suite instead of skipping those tests.

Example Configuration

"env": {
  "LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
  "LINEAI_USERNAME": "<my username>",
  "LINEAI_PASSWORD": "<my password>",
  "LINEAI_WORKSPACE_NAME": "<my workspace>",
  "LINEAI_DEBUG_MODE": "true"
}

Pinning the version

instead of using the latest version of the server, you can pin to a specific version by changing the args field to match the version in pypi e.g.

    "args": [
      "[email protected]"
    ],

Version Compatibility

This MCP server has the following version compatibility requirements:

• Version 0.3.1 and below: Compatible with all Lineai API versions
• Version 0.4.0 and above: Requires Lineai API version 25.10.0 or greater

If you're upgrading, make sure your Lineai server meets the minimum API version requirement.

Graph tools: Require your Lineai deployment to serve the graph endpoints under /api/ai-retrieval/graph/. Older or partial deployments may return 404; the MCP tools surface that as a clear error instead of opaque failures.

Debug Logging

When LINEAI_DEBUG_MODE=true, debug files are written to the system temporary directory:

• Windows: %TEMP%\lineai-mcp-server (typically C:\Users\{username}\AppData\Local\Temp\lineai-mcp-server)
• macOS: /tmp/lineai-mcp-server (or $TMPDIR/lineai-mcp-server if set)
• Linux: /tmp/lineai-mcp-server (or $TMPDIR/lineai-mcp-server if set)

Debug files include:

• timing_log.txt - Performance timing information
• impact_data_*.json - Raw impact analysis data for troubleshooting

Finding your log directory:

import tempfile
import os
print("Log directory:", os.path.join(tempfile.gettempdir(), "lineai-mcp-server"))

Testing

Running Unit Tests

The project uses unittest for testing. You can run unit tests without any external dependencies:

python -m unittest discover -s test -p "unit_*.py"

Unit tests use mock data and don't require a connection to a Lineai server.

Integration Tests (Optional)

If you want to run integration tests that connect to a real Lineai server:

1. Copy test/.env.test.example to test/.env.test and populate with your Lineai server details
2. Run the integration tests:

python -m unittest discover -s test -p "integration_*.py"

Note: Integration tests require access to a Lineai server instance.

Graph MCP end-to-end tests

test/integration_test_graph.py drives the real MCP handler path (handle_call_tool) for lineai-graph-capabilities and a chained flow (search → impact → path → validate → owners) against LINEAI_SERVER_HOST. Configure credentials the same way as other integration tests (test/.env.test from test/.env.test.example).

• If the host does not expose graph routes, tests skip by default.
• Set LINEAI_GRAPH_E2E_REQUIRED=1 to turn missing graph APIs into hard failures (useful in CI when graph must be present).

From the repo root:

./scripts/run_graph_e2e.sh

Equivalent:

uv run python -m unittest test.integration_test_graph -v

Validation for Official MCP Registry

mcp-name: io.github.lineai-intelligence/lineai-mcp-server