ai·rete·rag MCP Server
Use ai·rete·rag — deterministic rule-based decisions with RAG-powered explanations — from Claude Code, Claude Desktop, or any MCP client.
The verdict always comes from the Rete rule engine (auditable, reproducible); the LLM only explains why, grounded in your ingested policy documents.
Tools
| Tool | What it does |
|---|---|
decide |
Make a decision in a domain — structured facts and/or free text, with optional Pattern 01 (rules scope retrieval) and Pattern 02 (retrieval into working memory). as_of reads the policy documents in force on an earlier day |
get_parameters |
The facts a domain's rules decide on (from your account's own rules), most decisive first — what to collect before decide |
list_rules |
Inspect a domain's rules — conditions, verdicts, salience, overlaps |
get_rule_source |
Fetch a domain's rule set as editable YAML |
import_policy_rules |
Turn a written policy document into draft rules, each citing the sentence it encodes (nothing is saved — review, then put_rules) |
put_rules |
Create or replace a domain's rule set from YAML (dry_run to validate) |
ingest_text |
Add policy text to a domain's knowledge base, optionally dated (effective_from, effective_until) or replacing an earlier version (supersedes) |
list_documents |
Browse a domain's ingested documents |
get_usage |
Check your plan and remaining monthly decision quota |
Prompt: decide_case
An agent workflow for deciding a case in any domain on your account, including
your own: it calls get_parameters to learn which facts the rules decide on,
reads the case — text, photos, document scans, voice notes or video (the agent
reads the media itself; the API only receives the facts) — maps it onto those
exact fields, calls decide with fact_sources recording which file each fact
came from (kept in the decision's audit trail), and — while the result's
missing_parameters is non-empty (facts that could still change the verdict)
— asks you for them, most decisive first, and decides again. The verdict always
comes from the rules.
Connect by URL (no install)
The server is also hosted, which is the only route for clients that connect to a URL and have no field for a static header — claude.ai and Claude Desktop custom connectors, in particular.
| URL | Auth | Reaches |
|---|---|---|
https://ai-rete-rag.com/mcp/auth |
Sign in with Google, once, in the browser | Your account — your domains, your plan quota |
https://ai-rete-rag.com/mcp |
None, or Authorization: Bearer ik_... |
Shared demo domains anonymously; your account with a key |
Add https://ai-rete-rag.com/mcp/auth as a custom connector and approve the
prompt. Connections are listed under Settings → Connected apps, and
disconnecting one takes effect immediately.
Install
No install needed with uv — uvx ai-rete-rag-mcp
fetches and runs the server on demand (see the config snippets below).
Alternatively, install it as a package:
pip install ai-rete-rag-mcp # from PyPI
pip install . # or from source, in this repo
Configure
First create an API key: sign in at ai-rete-rag.com,
open Settings → API Keys, and create a key (ik_... — shown once).
Claude Code
claude mcp add ai-rete-rag -e AI_RETE_RAG_API_KEY=ik_your-key-here -- uvx ai-rete-rag-mcp
(If you installed via pip, use -- ai-rete-rag-mcp instead of -- uvx ai-rete-rag-mcp.)
Or skip the install entirely and point it at the hosted endpoint with your key:
claude mcp add --transport http ai-rete-rag https://ai-rete-rag.com/mcp \
--header "Authorization: Bearer ik_your-key-here"
Claude Desktop / other clients (JSON)
{
"mcpServers": {
"ai-rete-rag": {
"command": "uvx",
"args": ["ai-rete-rag-mcp"],
"env": {
"AI_RETE_RAG_API_KEY": "ik_your-key-here"
}
}
}
}
(With a pip install, set "command": "ai-rete-rag-mcp" and drop "args".)
Environment variables:
| Variable | Default | Purpose |
|---|---|---|
AI_RETE_RAG_API_KEY |
(none) | Your API key — authenticates calls and ties them to your plan quota |
AI_RETE_RAG_API_URL |
https://ai-rete-rag.com |
API base URL — point at http://localhost:8000 for local dev |
Without a key you can still explore the shared demo domains (loan, fraud,
clinical, …) subject to free-tier limits.
Example
"Use ai·rete·rag to decide whether this loan application should be approved: credit score 645, annual income $52k, requested amount $30k."
Claude calls decide(domain="loan", facts={...}) and returns the rule-derived
verdict plus a plain-English explanation citing the underwriting policy.
Metadata
Release files for ai-rete-rag-mcp 0.10.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ai_rete_rag_mcp-0.10.0.tar.gz | 25.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ai_rete_rag_mcp-0.10.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 45.1 kB
Release files / ai_rete_rag_mcp-0.10.0.tar.gz
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Release files / ai_rete_rag_mcp-0.10.0-py3-none-any.whl
| Download URL | ai_rete_rag_mcp-0.10.0-py3-none-any.whl |
|---|---|
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| Tags | Python 3 |
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