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Show HN: EnrichMCP – A Python ORM for Agents

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Re: Show HN: EnrichMCP – A Python ORM for Agents

#11

Post by polskibus »

simba-k wrote: post #7
polskibus wrote: post #6

This looks very interesting but I’m not sure how to use it well. Would you mind sharing some prompts that use it and solve a real problem that you encountered ?

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

are you saying that a current gen LLM can answer such queries with EnrichMCP directly? or does it need guidance via prompts (for example tell it which tables to look at, etc. ) ? I did expose a db schema to LLM before, and it was ok-ish, however often times the devil was in the details (one join wrong, etc.), causing the whole thing to deliver junk answers.

what is your experience with non trivial db schemas?

Re: Show HN: EnrichMCP – A Python ORM for Agents

#12

Post by simba-k »

polskibus wrote: post #11
simba-k wrote: post #7

Earlier quoted context omitted.

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

are you saying that a current gen LLM can answer such queries with EnrichMCP directly? or does it need guidance via prompts (for example tell it which tables to look at, etc. ) ? I did expose a db schema to LLM before, and it was ok-ish, however often times the devil was in the details (one join wrong, etc.), causing the whole thing to deliver junk answers. what is your experience with non trivial db schemas?

So one big difference is that we aren't doing text2sql here, and the framework requires clear descriptions on all fields, entities, and relationships (it literally won't run otherwise).

We also generate a few tools for the LLM specifically to explain the data model to it. It works quite well, even on complex schemas.

The use case is more transactional than analytical, though we've seen it used for both.

I recommend running the openai_chat_agent in examples/ (also supports ollama for local run) and connect it to the shop_api server and ask it a question like : "Find and explain fraud transactions"

Re: Show HN: EnrichMCP – A Python ORM for Agents

#13

Post by smohare »

simba-k wrote: post #7
polskibus wrote: post #6

This looks very interesting but I’m not sure how to use it well. Would you mind sharing some prompts that use it and solve a real problem that you encountered ?

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

[dead]

Re: Show HN: EnrichMCP – A Python ORM for Agents

#14

Post by simba-k »

skuenzli wrote: post #10
simba-k wrote: post #7

Earlier quoted context omitted.

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

This is the motivating example I was looking for on the readme: a client making a request and an agent handling it using the MCP. Along with a log of the agent reasoning its way to the answer.

Yes but the agent reasoning is going to use an LLM, I sometimes run our openai_chat_agent example just to test things out. Try giving it a shot, ask it to do something then ask it to explain its tool use.

Obviously, it can (and sometimes will) hallucinate and make up why its using a tool. The thing is, we don't really have true LLM explainability so this is the best we can really do.

Re: Show HN: EnrichMCP – A Python ORM for Agents

#15

Post by polskibus »

simba-k wrote: post #12
polskibus wrote: post #11

Earlier quoted context omitted.

are you saying that a current gen LLM can answer such queries with EnrichMCP directly? or does it need guidance via prompts (for example tell it which tables to look at, etc. ) ? I did expose a db schema to LLM before, and it was ok-ish, however often times the devil was in the details (one join wrong, etc.), causing the whole thing to deliver junk answers. what is your experience with non trivial db schemas?

So one big difference is that we aren't doing text2sql here, and the framework requires clear descriptions on all fields, entities, and relationships (it literally won't run otherwise). We also generate a few tools for the LLM specifically to explain the data model to it. It works quite well, even on complex schemas. The use case is more transactional than analytical, though we've seen it used for both. I recommend r…

So explicit model description (kind of repeating the schema into explicit model definition) provides better results when used with LLM because it’s closer to the business domain(or maybe the extra step from DDL to business model is what confuses the LLM?). I think I’m failing to grasp why does this approach work better than straight schema fed to Llm.

Re: Show HN: EnrichMCP – A Python ORM for Agents

#17

Post by traverseda »

ljm wrote: post #16

> agents query production systems How do you handle PII or other sensitive data that the LLM shouldn’t know or care about?

That's an odd question. If you have a regular ORM how do you handle sensitive data that your user shouldn't know about? You add some logic or filters so that the user can only query their own data, or other data they have permission to access.

It's also addressed directly in the README. https://github.com/featureform/enrichmcp?tab=readme-ov-file#...

I know LLMs can be scary, but this is the same problem that any ORM or program that handles user data would deal with.

Re: Show HN: EnrichMCP – A Python ORM for Agents

#18

Post by Sytten »

simba-k wrote: post #7
polskibus wrote: post #6

This looks very interesting but I’m not sure how to use it well. Would you mind sharing some prompts that use it and solve a real problem that you encountered ?

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

This is opening a new can of worm of information disclosure, at least one job the AI won't kill is people in security.

MCP is the new IoT, where S stands for security /s

Re: Show HN: EnrichMCP – A Python ORM for Agents

#19

Post by TZubiri »

simba-k wrote: post #7
polskibus wrote: post #6

This looks very interesting but I’m not sure how to use it well. Would you mind sharing some prompts that use it and solve a real problem that you encountered ?

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

Cool. Can you give the agent a db user with restricted read permissions?

Also, generic db question, but can you protect against resource overconsumption? Like if the junior/agent makes a query with 100 joins, can a marshall kill the process and time it out?

Re: Show HN: EnrichMCP – A Python ORM for Agents

#20

Post by TZubiri »

Sytten wrote: post #18
simba-k wrote: post #7

Earlier quoted context omitted.

Imagine you're building a support agent for DoorDash. A user asks, "Why is my order an hour late?" Most teams today would build a RAG system that surfaces a help center article saying something like, "Here are common reasons orders might be delayed." That doesn't actually solve the problem. What you really need is access to internal systems. The agent should be able to look up the order, check the courier status, pul…

This is opening a new can of worm of information disclosure, at least one job the AI won't kill is people in security. MCP is the new IoT, where S stands for security /s

What is the difference between a junior and an agent. Can't you give them smart permissions on a need to know basis?

I guess you also need per user contexts, such that you depend on the user auth to access user data, and the agent can only access that data.

But this same concern exists for employees in big corps. If I work at google, I probably am not able to access arbitrary data, so I can't leak it.

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