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OloLand M&A intelligence — 33 tools for deal analysis, valuation, risk, and cross-deal learning.
io.github.aniebyl/ololand-dd is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 67/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding io.github.aniebyl/ololand-dd to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
mcp add io.github.aniebyl/ololand-dd https://ma-workbench-api-303576587005.us-central1.run.app/mcpReview the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-04-01T21:18:26.931413Z",
"publishedAt": "2026-04-01T21:18:26.931413Z",
"statusChangedAt": "2026-04-01T21:18:26.931413Z"
}
},
"server": {
"name": "io.github.aniebyl/ololand-dd",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-09-29/server.schema.json",
"remotes": [
{
"url": "https://ma-workbench-api-303576587005.us-central1.run.app/mcp",
"type": "streamable-http"
}
],
"version": "1.0.1",
"packages": [
{
"version": "1.0.1",
"transport": {
"type": "stdio"
},
"identifier": "@ololand/mcp-server",
"registryType": "npm"
}
],
"repository": {
"url": "https://github.com/ololand-ai/ololand-dd-plugin",
"source": "github"
},
"description": "OloLand M&A intelligence — 33 tools for deal analysis, valuation, risk, and cross-deal learning."
}
}