MCP server for the Agimus data platform — connects AI coding tools to your ontology
Project description
Agimus MCP Server
Connects AI coding tools — Claude Desktop, Claude Code, Cursor, OpenAI Codex, and other Model Context Protocol clients — to the Agimus data platform. The server gives the AI live awareness of your ontology (entities, properties, links, categories, types) and bundled reference docs for the Agimus Python SDK, so it writes correct code instead of guessing field names.
What the AI gets
Tools (16)
Discovery:
| Tool | What it does |
|---|---|
agimus_semantic_schema_search |
Natural-language search over entity/property/link descriptions; optional categories=[...] to scope by domain |
agimus_list_entities |
Every entity, with PK, property/link counts, and category memberships; optional categories=[...] to filter |
agimus_get_entity_schema |
Full schema for one entity (properties, rich link details, category memberships) |
agimus_list_links |
Every link in the ontology (both directions per link) |
agimus_get_link |
Full details for a link by api_name |
agimus_list_categories |
Every category in the tenant with access-filtered entity counts |
agimus_get_category |
Drill into one category — its entities (access-filtered) |
agimus_list_datasets |
All datasets, optionally filtered by source/name |
agimus_get_dataset_schema |
Columns, types, row count + SDK usage snippet |
agimus_whoami |
Tenant name, scope, rate limit for the current API key |
Read (read-only — writes happen via the SDK in user code):
| Tool | What it does |
|---|---|
agimus_query_objects |
Filter / sort / project / expand / paginate |
agimus_get_object |
Single object by primary key |
agimus_get_related_objects |
Traverse a link from one object (cursor-paginated) |
agimus_count_objects |
Cheap COUNT(*) with optional filter |
agimus_distinct_values |
Distinct values, optionally with frequency counts |
agimus_aggregate |
group_by + metrics (count/sum/avg/min/max/...) |
Resources
agimus://schema/entities/{entity}— live, full schema for one entityagimus://docs/sdk-reference— the full Agimus Python SDK reference (sourced directly from the SDK's README so it never drifts)
Install & configure
The recommended path is uvx — it auto-installs
the latest version on first run and keeps it isolated. No prior pip install
is required.
Claude Code
Add to ~/.claude/claude_code_config.json:
{
"mcpServers": {
"agimus": {
"command": "uvx",
"args": ["agimus-mcp"],
"env": { "AGIMUS_API_KEY": "agm_your_api_key_here" }
}
}
}
Cursor
Create .cursor/mcp.json in your project root:
{
"mcpServers": {
"agimus": {
"command": "uvx",
"args": ["agimus-mcp"],
"env": { "AGIMUS_API_KEY": "agm_your_api_key_here" }
}
}
}
OpenAI Codex
Add to ~/.codex/config.toml:
[mcp_servers.agimus]
command = "uvx"
args = ["agimus-mcp"]
env = { AGIMUS_API_KEY = "agm_your_api_key_here" }
Claude Desktop
Add to claude_desktop_config.json (Claude → Settings → Developer → Edit Config):
{
"mcpServers": {
"agimus": {
"command": "uvx",
"args": ["agimus-mcp"],
"env": { "AGIMUS_API_KEY": "agm_your_api_key_here" }
}
}
}
Alternative install: pip
If you prefer a pinned global install:
pip install agimus-mcp
Then replace "command": "uvx", "args": ["agimus-mcp"] with
"command": "agimus-mcp" in any config above.
CLI options
agimus-mcp [--api-key agm_xxx] [--base-url URL] [--timeout SECONDS] [--log-level LEVEL]
Every flag has an environment-variable equivalent:
| Flag | Env var | Default |
|---|---|---|
--api-key |
AGIMUS_API_KEY |
(required) |
--base-url |
AGIMUS_BASE_URL |
https://api.agimus.ai |
--timeout |
AGIMUS_TIMEOUT |
30 |
--log-level |
AGIMUS_LOG_LEVEL |
WARNING |
Logs are written to stderr — stdout is reserved for the MCP stdio protocol.
License
MIT
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