Recordant
Patent-pending diagnostic + decisions-ledger engine for any host software system — operator briefs and a severity-gated MCP console.
README
Recordant
<!-- mcp-name: io.github.recordant/recordant -->
Recordant is a patent-pending diagnostic + decisions-ledger engine for any host software system. Point it at a target's repo, changelog, and logs, and it runs a periodic scan that produces an operator brief: what changed, what looks broken, what was fixed, and a prioritized diagnosis list — plus a chat-native MCP console over the same data.
Bring your own LLM (Anthropic, OpenAI, or Gemini — your key, your usage, your bill). Recordant makes no provider assumption.
15-minute install and first brief
-
Install the package and your LLM provider's SDK (only the one you use):
pip install "recordant[anthropic]" # or [openai], [gemini], or [all] -
Copy
recordant.example.tomltorecordant.tomland set:repo_root— the target repo to diagnosechangelog_path— usuallyCHANGELOG.md(falls back togit logif absent)log_globs— which log files to health-scan[llm]— your provider, model, andapi_key_env(the name of the environment variable holding your key — never the key itself)
-
Export your key and run one cycle:
export RECORDANT_LLM_KEY=sk-... recordant --config recordant.tomlThe brief path prints to stdout. The brief, snapshot, and (optional) decisions ledger are written under your configured
output_dir. -
Schedule it (cron / systemd timer / your own scheduler) at the
schedule_secondscadence in your config.
That's the whole loop. See ENGINE.md for the adapter architecture, config
reference, and known limitations.
MCP console
Recordant also runs as an MCP server exposing 7 tools over stdio, for use from a chat-native MCP client:
| Tool | Purpose |
|---|---|
recordant_brief |
Latest delta brief since the prior cycle |
recordant_status |
Current status snapshot (health, cadence, active book) |
recordant_digest |
Open items, Accepted-Risk Register, fixes shipped, deadlines |
recordant_open_items |
Every open diagnosis with age, recurrence, evidence |
recordant_acknowledge |
Acknowledge a diagnosis (P0 items cannot be acknowledged) |
recordant_accept_risk |
Pin a diagnosis in the Accepted-Risk Register |
recordant_resolve |
Assert a diagnosis is resolved (audited against the changelog) |
Token-auth is on by default — there is no anonymous mode. The server
requires a valid RECORDANT_LICENSE_KEY and verifies it against the license
service at startup before registering a single tool; it will not run
unlicensed, even briefly. A short offline-grace window covers a transient
network failure to the license service, but an explicitly invalid or expired
key is refused immediately.
export RECORDANT_LICENSE_KEY=rk-...
recordant-mcp --config recordant.toml
Add it to your MCP client's config (Claude Desktop, Claude Code, etc.):
{
"mcpServers": {
"recordant": {
"command": "recordant-mcp",
"args": ["--config", "/absolute/path/to/recordant.toml"],
"env": { "RECORDANT_LICENSE_KEY": "rk-..." }
}
}
}
Licensing and pricing
Recordant is licensed software, not open source (see LICENSE). Get a key
and see current pricing at
https://mentorsentinel.ai/markets-ai-governance.html.
Docker
Recordant ships as a compiled image -- there is no engine source to build
from, so docker build is not part of a customer workflow. Pull the
licensed image (published per BUILD-RELEASE.md) and run it:
docker pull recordant/recordant:<version>
docker run -v $PWD/recordant.toml:/app/recordant.toml:ro \
-v $PWD/target-repo:/app/target-repo:ro \
-e RECORDANT_LLM_KEY \
recordant/recordant:<version> --config recordant.toml
The image is signed (cosign) and ships with an SBOM (syft) so you can audit
what it does -- dependencies, egress -- without needing readable engine
source; see BUILD-RELEASE.md for verification commands.
See docker-compose.example.yml for a compose-based setup, including the
MCP console service.
Support
Found a bug or have a feature request? Open an issue — see
.github/ISSUE_TEMPLATE/. For licensing or account questions, use the
pricing page above.
Patents
Recordant's inference loop and decisions-ledger lifecycle are patent-pending (U.S. application numbers available on request). "Patent-pending" reflects filed, unexamined provisional applications — no patent has issued.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.