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ai·rete·rag MCP Server

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ai·rete·rag is answering right now. Last checked 7 min ago. 345 installs a week from pypi. It exposes 9 tools. Last commit 20 Oct 2026.

Author rules from policy docs, then decide: a Rete engine gives the verdict, an LLM explains why.

Installs per day peak 394 · avg 86 · -19% w/w
a month agotoday
Uptime history 89 days of history · worst day 94%
89 days agonow
100.0%
Uptime 24h
182 of 182 checks
9
Tools
read from the server
675 ms
Response time
average over 24h
345
Installs / week
npm and PyPI

What changed 12

Every tool that appeared, vanished or quietly changed what it asks for. Recorded since 27 September 2026. No other catalogue keeps this.

17 Oct a tool description was rewritten import_policy_rules
10 Oct 2 tool descriptions were rewritten decide, ingest_text
10 Oct 2 tools changed the parameters they ask for decide, ingest_text
3 Oct a tool appeared get_parameters
3 Oct a tool description was rewritten decide
3 Oct a tool changed the parameters it asks for decide
28 Sep a tool description was rewritten decide
27 Sep 3 tool descriptions were rewritten get_rule_source, ingest_text, put_rules

ai·rete·rag missed one check this week

Everything else answered, so this is steady rather than shaky. We check every 15 minutes, which is how a one-off gets told apart from the start of a pattern, and how you hear about the next one within the hour instead of from your users.

Three servers free · no card

Connect this server

Endpoint below is the one we actually reach during checks — not the one copied from a README. Last verified 7 min ago.

run in your terminal
claude mcp add ai-rete-rag-mcp --transport http https://ai-rete-rag.com/mcp
Settings → Connectors → Add custom connector, then paste this address
https://ai-rete-rag.com/mcp
~/.codex/config.toml
[mcp_servers.ai-rete-rag-mcp]
url = "https://ai-rete-rag.com/mcp"
.cursor/mcp.json
{
  "mcpServers": {
    "ai-rete-rag-mcp": {
      "url": "https://ai-rete-rag.com/mcp"
    }
  }
}
.vscode/mcp.json
{
  "servers": {
    "ai-rete-rag-mcp": {
      "type": "http",
      "url": "https://ai-rete-rag.com/mcp"
    }
  }
}

This endpoint answered with an authorization challenge. The server is running, and it signs you in through your browser: there is no API key to paste.

This server publishes 1 more address. The block above uses the one we reach during checks; the full list is under Endpoints below, and the author may intend a particular one for your client.

Available tools 9

Read directly from the server with tools/list, grouped by what they act on. If a tool disappears, we record the date.

decide
decide
Make a deterministic, auditable decision in a domain. The verdict comes from the domain's rule set (Rete engine, never the LLM), so it is reproducible and compliant. The explanation is generated from the domain's ingested policy documents. Without an API key only the shared demo domains are available, and the response carries `next_step`: how to get a free key for your own rules. Pass that on to the user rather than dropping it. Args: domain: Rule-set domain, e.g. "loan", "fraud", "clinical". query: Natural-language question or decision request. facts: Structured facts for working memory, e.g. {"credit_score": 710, "annual_income": 85000}. Use `get_parameters` to see which fields a domain's rules test. Facts you read yourself from photos, document scans, voice notes or video go here, under those field names; explicit facts always win over anything extracted server-side. fact_sources: Where each fact in `facts` came from, e.g. {"monthly_income": "payslip.jpg", "claim_usd": "voice note 1"}. Always set this for facts you read from a file, photo, recording or video: it is stored in the decision's audit record so a reviewer can tell typed facts from ones read off evidence. Facts without a source are recorded as "request". as_of: Read the domain's policy documents as of this day, as YYYY-MM-DD (default: today). Use the date a case was filed to decide it under the policy in force then; the rules themselves are always the current ones. unstructured_text: Optional free text (an application, a case note, a voice-note transcript, what a photo or video shows); facts are extracted from it automatically and merged. response_mode: "verdict_only" (fastest), "verdict_with_explanation", or "full_audit" (every rule evaluation + retrieved chunks, available on every plan including the free tier). rule_firings come back in causal order: a rule that matched a fact asserted by an earlier firing appears after it, with the derived facts listed under `asserted_facts`. filter_retrieval_with_rules: Pattern 01 — run the rules first and let a fired rule's `retrieval_scope` action narrow which documents the retrieval searches before it runs. extract_from_retrieval: Pattern 02 — parse the retrieved documents into facts and assert them into working memory, so rules fire on what was actually read (not just the facts you passed). The result's `missing_parameters` lists rule inputs that were not supplied but could still change the verdict, most decisive first. When it is non-empty, tell the user the verdict is provisional and ask for those facts; when it is empty, more facts cannot change the verdict.
documents
list_documents
List the documents ingested into a domain's knowledge base.
import
import_policy_rules
Convert a written policy document into DRAFT decision rules (LLM-assisted). Returns validated draft rules (when/action, including chained asserts where the policy stages its determinations), derived rule→rule edges, and overlap warnings. Each returned rule carries a `citation` field holding the policy sentence it encodes (also summarized in the top-level `citations` map). Coverage runs the other way too: `uncovered_sentences` lists policy sentences no rule's citation points back to (out of `policy_sentence_count`), and `unverified_citations` names rules whose quote is missing or not found verbatim in the policy. Show both to the user: an uncovered sentence may need a rule; an unverified one may be a paraphrase that misstates the policy. `background_sentences` is the subset of uncovered sentences with no obligation language (definitions, preamble); lead with the others, but mention these too, since the split is a keyword heuristic. NOTHING IS SAVED: review the drafts (and show them to the user), then persist explicitly with `put_rules` — validate first with dry_run=true, and keep each rule's `citation` in the YAML you save so the audit trail back to the policy survives. Args: domain: Domain the rules are drafted for (an owned domain or a new name). policy_text: The policy document text (max ~50k characters).
ingest
ingest_text
Add policy/reference text to a domain's knowledge base. The text is chunked and embedded; explanations for future decisions in this domain will cite it. Creating a new domain claims it for your account (plan limits apply). Ingesting into a built-in demo domain gives your account its own copy of that demo (it counts as one of your domains); the shared demo others see is unchanged. On team plans, only the domain admin (the member who created the domain, or the subscription owner) can add documents. Documents can be dated. A decision only cites documents in force on its date, so a replaced policy is never quoted for a new decision. To publish a new version, ingest it under a new `source` with `supersedes` naming the old one: the old document stops applying on the new one's `effective_from` (today if not given), and the new one starts that day. A document can be superseded only once; to replace a successor, supersede the successor. Args: domain: Domain to ingest into (existing or new). text: The policy or reference text. source: Optional source name shown in the document list. effective_from: First day this document applies, as YYYY-MM-DD (default: always). effective_until: First day it no longer applies, as YYYY-MM-DD (default: no end). supersedes: Source name of the document this one replaces.
parameters
get_parameters
List the input parameters a domain's rules decide on — the facts to collect before calling `decide`. Derived from your account's own copy of the rules, so it covers your custom domains too. Each parameter has its `field` name (the key to use in `facts`), `kind` (number / boolean / string), the `tests` rules make on it (e.g. >= 720 — match these units and scales), the `rules` and `verdicts` it can lead to, and `max_salience`. Parameters come most decisive first: those feeding the highest-salience rules, which win when several rules fire. Also returns every verdict the domain can reach.
put
put_rules
Create or replace a domain's rule set from YAML (self-serve rule authoring). The first save to a new domain claims it for your account (plan limits apply). Saving to a built-in demo domain saves your own copy of it, seeded from the demo; the shared demo others see is unchanged. Rules are validated before saving — set dry_run=true to validate without persisting. The response reports ok/errors, the parsed rules, and any overlap warnings. YAML format — a list of rules. Flat form (conditions are AND-ed): - name: "Approve" salience: 10 conditions: - type: loan field: credit_score op: ">=" value: 700 action: verdict: "APPROVED" reason: "Credit score meets threshold" Tree form — `when:` holds nested all/any/not condition groups, and an action may assert derived facts that other rules consume (forward chaining; the rule graph derives from these automatically): - name: "Sepsis Screen" salience: 30 when: all: - {type: clinical, field: temperature_f, op: ">=", value: 101.5} - any: - {type: clinical, field: wbc_count, op: ">", value: 12.0} - {type: clinical, field: bands_pct, op: ">", value: 10} action: verdict: "URGENT_ALERT" assert: - {type: sepsis_flag, fields: {severity: high}} - name: "Escalate" salience: 40 when: all: - {type: sepsis_flag, field: severity, op: "==", value: high} - {type: clinical, field: age, op: ">=", value: 65} action: verdict: "ADMIT_ICU" Use either `conditions:` or `when:` per rule, never both. `not` passes when the inner condition does not hold (including when the field is absent). Produce/consume cycles between rules are rejected at validation. An action may also carry `retrieval_scope: { <key>: <value> }` to narrow which documents retrieval searches (Pattern 01). A rule may also carry `citation:` — the policy sentence it encodes. It is stored with the rule and shown beside it in decision audits, so a verdict can be defended with the source language, not just the rule name: - name: "Decline Late Returns" salience: 20 citation: "Returns are accepted within 30 days of delivery." when: all: - {type: retail, field: days_since_delivery, op: ">", value: 30} action: verdict: "DENIED" IMPORTANT: when persisting drafts returned by `import_policy_rules`, copy each rule's `citation` through into this YAML. Dropping it silently loses the link from the decision back to the policy clause that justifies it. Args: domain: Domain to author (an owned domain, or a new name to claim). rules_yaml: The full rule set as YAML text. dry_run: Validate only, without saving.
rule
get_rule_source
Fetch a domain's rule set as editable YAML (plus the parsed rules and whether you may edit it). Use this before `put_rules` to see the current rules. For a built-in demo domain you get your own copy's rules once you have edited it, otherwise the shared demo's.
rules
list_rules
List the decision rules for one domain (or all domains). Returns each rule's conditions — either a flat AND list (field / operator / value) or a `when` condition tree (nested all/any/not) — plus its verdict, salience, and any asserted facts (`action.assert`, the facts a rule produces for other rules to consume). `edges` lists the derived rule→rule dependencies: src asserts a fact type that dst's conditions test (forward chaining). Each rule may also carry `citation` — the policy sentence it encodes — which is what lets a decision be traced back to the source clause. Also includes overlap warnings. Use this to learn which fact fields a domain expects before calling `decide`.
usage
get_usage
Show this account's decision usage, plan, and remaining monthly quota.

Endpoints

URLTransportStateLatencyChecked
https://ai-rete-rag.com/mcp streamable-http answering 644 ms 7 min ago
https://ai-rete-rag.com/mcp/auth streamable-http sign-in 633 ms 8 min ago

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ai·rete·rag — questions

Answers built from our own checks of this server.

What can ai·rete·rag do?
It exposes 9 tools, read directly from the server on our last check. Among them: decide, get_parameters, get_rule_source, get_usage, import_policy_rules, ingest_text and 3 more. The full list with descriptions is on this page — we take it from the server itself via tools/list, not from a README. How MCP servers expose tools in the first place →
Is ai·rete·rag working right now?
We send a real MCP handshake every 15 minutes. Over the last 24 hours 182 of 182 checks got a reply (100.0%), average response time 675 ms. The bar chart above shows every period we have measured.
How do I connect ai·rete·rag?
Copy the ready config from this page. We generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each in the form that client actually reads. It is a remote server, so there is nothing to install — the client connects to the address. You sign in through your browser once and the client keeps the session: no key to copy, and it refuses anonymous clients.
How do you sign in to ai·rete·rag?
No API key is involved. ai·rete·rag answers our knock with an OAuth challenge, so you authorise it once in your browser and the client keeps the session. That is also why we see no tool list: it will not describe itself to an anonymous client, and that is the server working as intended, not a fault.
How fast is ai·rete·rag?
It answers our handshake in 675 ms on average, which is faster than 14% of all working MCP servers we measure. That is on the slow side — worth knowing if the tool sits inside an interactive loop. The comparison comes from our own checks across the whole registry, every 15 minutes.
How many people use ai·rete·rag?
The pypi package ai-rete-rag-mcp was installed 345 times in the last week. Week over week that is -19%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.
Is ai·rete·rag open source?
The code is open source: it is published under the MIT licence, written in Python, 0 stars on GitHub and 1 open issue. The licence covers the code in the repository, not the hosted service at ai-rete-rag.com, which runs on its operator's own terms.