An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.
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io.github.lineai-intelligence/lineai-mcp-server is an MCP server that an MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 82/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"lineai-mcp-server": {
"env": {
"LINEAI_PASSWORD": "<my password>",
"LINEAI_USERNAME": "<my username>",
"LINEAI_DEBUG_MODE": "true",
"LINEAI_SERVER_HOST": "<url to the server e.g. https://myco.app.lineai.net>",
"LINEAI_WORKSPACE_NAME": "<my workspace>"
},
"args": [
"--directory",
"<PATH_TO_THIS_REPO>/lineai-mcp-server-main",
"run",
"lineai-mcp-server"
],
"type": "stdio",
"command": "<PATH_TO_UV>/uv"
}
}
}Are you the author?
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An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.
Run this in your terminal to verify the server starts. Then let us know if it worked - your result helps other developers.
uvx 'lineai-mcp-server' 2>&1 | head -1 && echo "✓ Server started successfully"
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No known CVEs.
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An MCP Server to utilize Lineai's rich software dependency data in your AI programming assistant.
Code-derived architecture documentation lives in docs/ (added in
LIN-737; updated for the LIN-699 changes — PR #61 / neo4cape #1860):
/api/... endpoints each tool calls, auth flow, caching, error-handling taxonomy, configuration, packaging/release, and findings.The server implements eight tools: two impact tools plus six graph tools backed by the Lineai graph HTTP API.
com.acme.shell.AnalyzeCommands) when the simple name may exist in several repositories: a fully-qualified class selects exactly that class, while a simple name that matches several classes returns an ambiguity page listing the candidates instead of silently analyzing another repository's class.depth argument, default 3, max 10). The report's Impact scope section says where the data came from (graph, or the legacy full-chain endpoint when the graph endpoint is not deployed) and whether it is complete (ok) or a lower bound (partial) — a partial result is never "no impact".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.
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).
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).query / q and/or identity_prefix; optional scan_space (scan space id or display name), prefer_latest_scan, limit. Results are ranked by the server (exact short name → exact fully-qualified name → class-like labels → text score → newest scan) and data.hits[i] describes data.nodes[i] (score, matchKind, labelTier); data.totalHitCount is the real total, so a full page means "narrow the query", not "absent".seed_node_ids with direction (upstream = what depends on the seeds, downstream, both) and depth (server default 3, max 10). The response carries status (ok | partial), truncated, nodeCount, staticAnalysisGap and assumptionsApplied markers; the tool prints a note above the JSON whenever the result is partial, degraded, or an older server ignored an option.from_node_id and to_node_id.