Marvnor
Hosted MCP Server for Agent Memory + HTTP API
Marvnor is a proprietary hosted MCP server and HTTP API for agent memory and record-based verification. Store supplied facts as source, relation and target records; ask structured questions against those records and receive TRUE, FALSE or UNKNOWN, with conflicting values flagged. Correct records in place or delete selected records. Send the current question and relevant Marvnor results to your model to write the answer.
Use it to check project dependencies, business states, time-sensitive records and conflicting information. Keep confirmed records between questions and pass only the results needed for the next answer.
Create a test Key · Quickstart · API reference · Recorded demo
Get started
Create a trial Key, then connect your existing AI. Follow the quickstart for setup, the working rule and everyday examples. Five tools handle saving, retrieval, verification, correction and deletion.
- MCP endpoint:
https://api.marvnor.com/v1/mcp - Authentication:
Authorization: Bearer <MARVNOR_API_KEY>
Use one Key per independent project. Your AI saves confirmed or sourced facts and queries Marvnor when it needs project memory. Tasks that do not depend on saved facts stay with your existing AI and tools. Usage test evidence.
Prefer direct HTTP? Run the Python example to save, verify, resolve a conflict, correct and delete its own test data. No SDK or dependencies are required.
Latest news, October 10: Marvnor reports stronger record-based judgments and a 98.2% input reduction in a long-history test · 中文新闻稿
Results that show the difference
October 10, 2026 rerun:
The public suite returned 1,227/1,227 expected verdicts across 62 requests, covering input variants and repeated workloads. Read the report and every comparison question or inspect the data.
中文使用说明 · Test index and archives · News · Public materials license
Send feedback and test results to [email protected]. Keep account keys and private customer material out of feedback.
Building an agent or project-memory tool? Bring one concrete use case and try the API with your own non-sensitive sample data.