Network Request & Response Copier: An OData-Friendly DevTools Request Viewer

Thu, 05 Feb 2026 09:30:00 +0100

AI assistants already help a lot, but they work best when you can give them good context. In web apps, the most important context is usually the real traffic between frontend and backend: request URLs, payloads, and responses.

When debugging, I want to copy that information quickly and completely. And since I mostly work with UI5 and OData, I usually need the full URL plus request and response body. In Chrome DevTools that often means a lot of manuall copy pasting, and if you need more than one request, it gets annoying fast.

So I did the obvious thing: I built a small Chrome DevTools extension with AI help that solves this problem for me. Yes, I could also use MCP tooling or an automated browser to extract network data, but for day-to-day debugging I care about speed and I usually know exactly which requests matter.

After using it locally for a while, I decided to publish my first extension in the Chrome Web Store.

Install it here: Chrome Web Store

Update (2026-02-10): Also available in the Microsoft Edge Add-ons store.

Source code: GitHub

The solution

Network Request & Response Copier adds a dedicated panel to DevTools that shows network entries in a clean, sortable list.

Left panel: sortable request list with filters, a payload preview, and pinning
Right panel: request payload (top) and response body (bottom)
Copy buttons: copy the selected entry, all filtered entries, or all pinned entries

And the part that matters most for my work: it makes OData traffic much easier to work with, especially $batch requests.

Key features

Copy in one click: Copy the selected entry with Ctrl/Cmd+C, or export all filtered or pinned entries. Output includes method, URL, payload, and response in one readable block.
OData $batch parsing: Splits multipart $batch into per-operation blocks and shows a quick “what’s inside” preview in the list (for example: PATCH MockConfig | GET Users).
Better OData overview: Keeps long $select and $filter URLs readable and shows request and response side-by-side, which makes sharing context (and debugging) much faster.
WebSocket included: Captures sent and received messages and shows them alongside HTTP entries.
DevTools-like workflow: Sort, search, filter by method, resize panes, pin important calls.
Stays fast: 500-entry cap, throttled UI updates, cached parsing.

How it works (short version)

Runs as a DevTools panel, so it only exists while DevTools is open.
Collects HTTP traffic via chrome.devtools.network.onRequestFinished (HAR entries).
Detects and parses $batch payloads to show a per-operation overview.
Captures WebSocket messages by instrumenting WebSocket in the inspected page.
Copies to clipboard with fallbacks that work in the DevTools environment.

You’re welcome to use it and let me know what you think, try it out suggest changes!

Supercharged Local Open Source Joule for Consultants: Search Your Data & Code

Wed, 29 Oct 2025 09:30:00 +0100

This post is a mirrored copy of my LinkedIn article, kept here so it remains searchable and independent from external platforms. You can still find the original on LinkedIn: LinkedIn Pulse article.

Joule for Consultants costs 250 € and mainly searches SAP Help, SAP Community and SAP for Me (Notes). That is useful, but it stops where real projects start: your own systems, codebase and sensitive data. Even though public assistants like ChatGPT and Claude can already reach SAP Help and SAP Community, the real gap is internal/authenticated data and code.

For this case I created a LibreChat fork that includes and auto-starts SAP MCP servers (Docs, Notes, S/4 OData and ABAP ADT) via Docker. So the MCP Server are local inside your Docker and no external MCP Server is used. LibreChat is an open‑source local chat UI with MCP support and built‑in Ollama integration, and it also supports many other model providers. You get a private assistant that can read official docs, inspect code and data from your systems without sending customer context to third‑party clouds. The fork’s documentation is here: sap‑mcp‑docs.

Running the assistant locally means the client, tools and, if you choose, the model stay on your machine (LibreChat supports Ollama), so you avoid pasting sensitive context into public chats. By adding S/4 OData and ABAP ADT, you bring real metadata, code and data into the loop.

Which MCP servers are used here?

The SAP Docs MCP provides unified search and fetch across SAPUI5/OpenUI5, CAP, ABAP keyword docs, SAP Help and SAP Community. The SAP Notes MCP is for searching and fetching SAP Notes when you have an error, OSS number or concrete symptom; it requires an S‑User certificate (Passport). The ABAP ADT MCP reads ABAP sources, DDIC structures and tables content via ADT APIs so you never paste code into chat and still get precise context. The S/4 OData MCP discovers services and exposes safe CRUD tools so your assistant can read and write via approved APIs.

How this works in practice

In my example I used o4‑mini from OpenAI, but LibreChat supports many model providers including OpenAI, Anthropic, Google, Azure AI Foundry and even SAP AI Core (not tested by me). The key point: you can also use a local model via Ollama. Depending on your use case, a local model is smart enough to call the right tools and MCP servers. I also use an ABAP Cloud Developer Trial instance connected via the S/4 OData and ADT MCP servers.

SAP Notes lookup + ABAP table verification

This first example flow combines the SAP Notes MCP and ABAP ADT MCP. First the assistant searches and fetches a specific SAP Note (sap_note_search, sap_note_get), then verifies the relevant DDIC table and fields via ADT. This confirms the symptom and the fix steps before proposing a safe, auditable corrective action (for example, an OData action). This is the pattern for many “error → note → verify → fix” tasks.

Service metadata + ValueList guidance

The assistant uses the S/4 MCP to discover the Football service and inspect entity schema (search-sap-services, discover-service-entities, get-entity-schema). It then pivots to the SAP Docs MCP to sap_help_search and sap_help_get the ValueList documentation and shows exactly where to add annotations. This marries live service metadata with the official docs so consultants can implement confidently, then validate in a Fiori app.

CDS view Value Help research

For CDS‑centric cases, the assistant focuses on SAP Docs MCP to collect the canonical examples for Common.ValueList and relevant ABAP/CDS annotations. If needed, ABAP ADT MCP can retrieve the current CDS definition (GetStructure/GetInclude) to compare the as‑is state and generate the minimal edit plan.

Other options and clients

There are other options and clients to use these MCP servers. If you prefer the Microsoft ecosystem, Visual Studio Code’s GitHub Copilot Chat supports MCP servers so you can connect the same SAP MCP endpoints there, as described in the VS Code Copilot Chat MCP guide. This is a simpler setup compared to the LibreChat fork. For a more governed setup you can use Copilot Studio together with the hosted S/4 OData MCP server from Nessi NV and the SAP Docs MCP Server.

I talked about these MCP servers and showed a live example of Copilot Studio and other LLM clients at SAP Inside Track Munich 2025. The recording is here: SAP Inside Track Munich 2025.

You can also build your own LLM client and use the MCP servers directly. I tried a quick PoC with the current libraries and tools. It worked, but turning it into a production‑ready assistant with robust MCP support is non‑trivial; I would not recommend rolling your own for production. The PoC is here: Custom LLM Client.

Getting started

To run this on your own machine, clone the repository, configure the .env and librechat.yaml files and start the Docker container. The docs are in this folder and the setup guide is here: MCP_SETUP_GUIDE.md.

Minimal path (see guides for details):

# Clone and configure
git clone https://github.com/marianfoo/local-llm-client-for-sap-consultants-librechat
cd local-llm-client-for-sap-consultants-librechat
cp .env.example .env
cp librechat.example.yaml librechat.yaml

# Configure the .env and librechat.yaml files

# Start in Docker
docker compose up -d

Security: running AI and MCP locally

Because the client, MCP server and (optionally) the model run on your machine, prompts, retrieved docs and tool results stay inside your environment. When you do connect to external sources, do it intentionally and stick to trusted endpoints. Operationally keep it simple and start read-only by default and enable write tools only when you require it. The short version: a local, open-source stack is the safer option here; unlike cloud assistants, your information never leaves your machine unless you choose to send it.

Conclusion

With MCP servers, LLM clients become smarter and more useful in real projects. LLMs simply cannot know everything, especially internal context that lives on enterprise networks and changes quickly. Because the Model Context Protocol is still relatively new in the enterprise, treat adoption with care and integrate it securely. Keeping information on your own machine and using open-source libraries lets you see exactly what runs and how.

SAP clearly wants to position Joule for Consultants as a unique product; today it still lags behind community projects for these use cases. At TechEd next week we’ll see whether community MCP servers are embraced and whether SAP’s own products improve enough to justify the price.

References

Custom LLM Client PoC: https://github.com/marianfoo/custom-llm-client
SAP Inside Track Munich 2025 session: https://www.youtube.com/watch?v=vTZCeaxsOwU
SAP partner listing (S/4 integration via OAuth): https://www.sap.com/products/artificial-intelligence/partners/nessi-nv-ai-data-enabler.html?countryCode=US
MCP clients overview (Claude Desktop etc.): https://modelcontextprotocol.io/clients/
Copilot Studio: https://adoption.microsoft.com/ai-agents/copilot-studio/
VS Code Copilot Chat MCP docs: https://code.visualstudio.com/docs/copilot/chat/mcp-servers
CAP MCP server (official): https://github.com/cap-js/mcp-server
CAP MCP plugin (community): https://github.com/gavdilabs/cap-mcp-plugin
S/4 OData MCP: https://github.com/lemaiwo/btp-sap-odata-to-mcp-server
ABAP ADT MCP: https://github.com/mario-andreschak/mcp-abap-adt
SAP Notes MCP: https://github.com/marianfoo/mcp-sap-notes
SAP Docs MCP: https://github.com/marianfoo/mcp-sap-docs
LibreChat Ollama guide: https://www.librechat.ai/blog/2024-03-02_ollama#getting-started-with-ollama
LibreChat model providers: https://www.librechat.ai/docs/configuration/pre_configured_ai
LibreChat website: https://www.librechat.ai/
LibreChat fork setup guide: https://github.com/marianfoo/local-llm-client-for-sap-consultants-librechat/blob/main/sap-mcp-docs/MCP_SETUP_GUIDE.md
LibreChat fork docs folder: https://github.com/marianfoo/local-llm-client-for-sap-consultants-librechat/tree/main/sap-mcp-docs
LibreChat fork (SAP consultants, with MCP servers): https://github.com/marianfoo/local-llm-client-for-sap-consultants-librechat

Odata on Marian Zeis Blog