codelogic-mcp-server
An MCP Server to utilize Codelogic's rich software dependency data in your AI programming assistant.
Documentation
lineai-mcp-server
An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.
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 and its associated "class".
- lineai-database-impact: Analyzes impacts between code and database entities.
- Takes the database entity type (column, table, or view) and its name.
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` defaults from `LINEAI_WORKSPACE_NAME` like other tools).
- 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 closedThis 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": "/uv",
"args": [
"--directory",
"/lineai-mcp-server-main",
"run",
"lineai-mcp-server"
],
"env": {
"LINEAI_SERVER_HOST": "",
"LINEAI_USERNAME": "",
"LINEAI_PASSWORD": "",
"LINEAI_WORKSPACE_NAME": "",
"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": "",
"LINEAI_USERNAME": "",
"LINEAI_PASSWORD": "",
"LINEAI_WORKSPACE_NAME": "",
"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": "",
"LINEAI_USERNAME": "",
"LINEAI_PASSWORD": "",
"LINEAI_WORKSPACE_NAME": ""
}
}
}> 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": "",
"LINEAI_USERNAME": "",
"LINEAI_PASSWORD": "",
"LINEAI_WORKSPACE_NAME": ""
}
}
}> 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": "",
"LINEAI_USERNAME": "",
"LINEAI_PASSWORD": "",
"LINEAI_WORKSPACE_NAME": "",
"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 impactsClaude 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 impactsWindsurf 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 impactsCursor 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 impactsEnvironment Variables
The following environment variables can be configured to customize the behavior of the server:
- `LINEAI_SERVER_HOST`: The URL of the Lineai server.
- `LINEAI_USERNAME`: Your Lineai username.
- `LINEAI_PASSWORD`: Your Lineai password.
- `LINEAI_WORKSPACE_NAME`: The name of the workspace to use.
- `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`.
Tests only
- `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": "",
"LINEAI_USERNAME": "",
"LINEAI_PASSWORD": "",
"LINEAI_WORKSPACE_NAME": "",
"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": [
"lineai-mcp-server@0.2.2"
],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.shEquivalent:
uv run python -m unittest test.integration_test_graph -vValidation for Official MCP Registry
mcp-name: io.github.lineai-intelligence/lineai-mcp-server
Frequently asked questions
What is codelogic-mcp-server?
codelogic-mcp-server is An MCP Server to utilize Codelogic's rich software dependency data in your AI programming assistant.
How do I install codelogic-mcp-server?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is codelogic-mcp-server open source?
Yes — it is hosted on GitHub at https://github.com/CodeLogicIncEngineering/codelogic-mcp-server and has 30 stars.
Related MCP tools
ACI.dev is the open source tool-calling platform that hooks up 600+ tools into any agentic IDE or custom AI agent through direct function calling or a unifie...
Universal memory layer for AI Agents; Announcing OpenMemory MCP - local and secure memory management. Python-based implementation.
An LLM agent that conducts deep research (local and web) on any given topic and generates a long report with citations. Built for the Model Context Protocol to
A powerful coding agent toolkit providing semantic retrieval and editing capabilities (MCP server & other integrations) Python-based implementation.
Expose your FastAPI endpoints as Model Context Protocol (MCP) tools, with Auth! Python-based implementation. Trusted by 11000+ developers.
Build effective agents using Model Context Protocol and simple workflow patterns Python-based implementation. Trusted by 7600+ developers.
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP