AI quoting agent for electronics distributors. Parses RFQ documents (PDF, Excel, Word) with Claude, looks up every line item against your SAP, Epicor, Oracle, or Dynamics 365 ERP via MCP connectors, and returns a priced draft quote in seconds. Self-hosted, MIT licensed.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Clear description
- ✓Topics declared
- ✓Documented (README)
claude mcp add electronics-rfq-agent -- python -m electronics-rfq-agent-cli{
"mcpServers": {
"electronics-rfq-agent": {
"command": "python",
"args": ["-m", "electronics-rfq-agent-cli"],
"env": {
"ANTHROPIC_API_KEY": "<anthropic_api_key>"
}
}
}
}ANTHROPIC_API_KEYMCP Servers overview
<!-- mcp-name: io.github.RudrenduPaul/electronics-rfq-agent -->
<div align="center">
# Electronics RFQ Agent
**Your sales engineers are spending 2-4 hours turning RFQ documents into quotes. This does it in seconds.**
Electronics RFQ Agent is a Python library and CLI that reads RFQ documents (PDF, Excel, Word), looks up every line item against your ERP catalog, and outputs a draft quote. It connects to SAP, Epicor, Oracle, and Microsoft Dynamics through MCP servers, so it works with Claude, GPT-4, or any agent framework that speaks MCP.
[](https://github.com/RudrenduPaul/electronics-rfq-agent/actions/workflows/ci.yml)
[](https://badge.fury.io/py/electronics-rfq-agent-cli)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://api.securityscorecards.dev/projects/github.com/RudrenduPaul/electronics-rfq-agent)
</div>
---

## Table of contents
- [Install](#install)
- [The problem this solves](#the-problem-this-solves)
- [Quickstart](#quickstart)
- [Commands](#commands)
- [API reference](#api-reference)
- [How it differs from the alternatives](#how-it-differs-from-the-alternatives)
- [ERP support](#erp-support)
- [Benchmarks](#benchmarks)
- [Integration matrix](#integration-matrix)
- [Try it in Docker](#try-it-in-docker)
- [Security](#security)
- [FAQ](#faq)
- [Contributing](#contributing)
- [License](#license)
## Install
```bash
pip install electronics-rfq-agent-cli
# or
uv add electronics-rfq-agent-cli
```
To install from source instead:
```bash
git clone https://github.com/RudrenduPaul/electronics-rfq-agent
cd electronics-rfq-agent
pip install -e .
# or, with uv:
uv sync
```
Parsing RFQ documents (PDF, Excel, Word) calls the Anthropic API, so set `ANTHROPIC_API_KEY` before running anything that touches a real document:
```bash
export ANTHROPIC_API_KEY="sk-ant-..."
```
You don't need this key to run `erfa audit` against an existing quote file, or to explore the CLI with `--help`. Only document parsing calls out to Claude.
## The problem this solves
We were working with electronics distributors who had 3-5 sales engineers spending most of their day on quote entry. Every tool we found was either tied to one specific ERP or required a 6-month integration project. We wanted something that worked with what distributors already had, could be self-hosted (quote data is sensitive), and was actually extensible.
The MCP architecture means adding a new ERP is writing one file. The parser handles the document formats distributors actually send: hand-filled PDFs, multi-sheet Excel files, and the occasional scanned table.
## Quickstart
No ERP system required to try it out; the mock backend ships with 200 realistic electronics parts. You do need `ANTHROPIC_API_KEY` set, since parsing the RFQ document is still a real Claude call:
```python
from electronics_rfq_agent import QuoteAgent
from electronics_rfq_agent.mcp.mock import MockERP
agent = QuoteAgent(erp=MockERP())
quote = agent.run_sync("path/to/rfq.xlsx")
for line in quote.lines:
print(f"{line.rfq_line.part_number}: {line.status} @ {line.unit_price}")
print(quote.summary())
```
MockERP applies quantity-based pricing tiers automatically: qty >= 1000 gets 20% off, qty >= 100 gets 10% off, qty >= 10 gets 5% off. List price applies below qty 10. This mirrors real-world volume pricing so benchmarks and integration tests reflect realistic cost curves.
Connect to a real ERP:
```python
from electronics_rfq_agent import QuoteAgent
from electronics_rfq_agent.mcp import EpicorMCP
agent = QuoteAgent(
erp=EpicorMCP(base_url="https://your-epicor.company.com", api_key="..."),
max_concurrent=10, # parallel ERP lookups (default: 10)
)
quote = agent.run_sync("rfq_2026_0619.xlsx")
print(quote.summary())
```
## Commands
`erfa` ships two subcommands. Every flag below is pulled straight from `erfa --help`.
| Command | Arguments | Flags | What it does |
|---|---|---|---|
| `erfa quote` | `rfq` (path, required) | `--mock`, `--margin <float>` (default `0.15`), `--output/-o <path>` | Parses an RFQ file and prints a draft quote. Needs `ANTHROPIC_API_KEY`; parsing always goes through Claude, `--mock` only swaps the ERP backend. |
| `erfa audit` | `quote_file` (path, required) | none | Prints a full audit report (found / substituted / not found, fill rate) for a quote JSON file saved with `erfa quote --output`. Reads a local file only, no API key needed. |
| `erfa mcp` | none | none | Launches an MCP stdio server exposing `quote_rfq`, `lookup_part`, and `audit_quote` as typed tools, so any MCP-compatible agent (Claude, GPT-4, Gemini) can call them directly without shelling out to the CLI. Point your MCP client config at `erfa mcp` as the command. |
```bash
# Generate a quote from an RFQ file against the mock ERP
export ANTHROPIC_API_KEY="sk-ant-..."
erfa quote rfq.xlsx --mock
# Save the quote as JSON for later inspection
erfa quote rfq.xlsx --mock --output quote.json
# Audit what happened: what was found, substituted, or missing and why
erfa audit quote.json
```
**Audit output example** (real output from `erfa audit docs/example-quote.json`, generated by running the mock ERP's pricing logic against the sample RFQ in `tests/fixtures/sample_rfq.txt`):
```
Audit Report - Quote df9fd083
RFQ Source : tests/fixtures/sample_rfq.txt
Lines : 5
Total : USD 64.04
FOUND (4)
------------------------------------------------------------
L 1 RES-0402-10K-1PCT qty=1000 unit=0.0064 ext=6.40
L 2 CAP-100NF-50V-X7R-0402 qty=500 unit=0.0104 ext=5.20
L 3 IC-LM358-SOIC8 qty=50 unit=0.7101 ext=35.50
L 4 XTAL-16MHZ-SMD qty=25 unit=0.6774 ext=16.94
NOT FOUND (1)
------------------------------------------------------------
L 5 MOSFET-NMOS-20V-3A-SOT23 Part 'MOSFET-NMOS-20V-3A-SOT23' not found in ERP catalog
Fill rate: 80% (4 found / 0 substituted / 1 not found)
```
> **Zero-price parts:** If a part exists in the ERP catalog but has a unit price of $0.00, the agent quotes $0 rather than skipping the line, and sets `line.notes` to a message flagging the zero price so you catch it before quoting the customer. Check `line.notes` for any found or substituted line before sending a quote out.
## API reference
The full reference lives in [docs/api.md](docs/api.md): every `QuoteAgent` parameter, the shared ERP connector interface, `Quote`/`QuoteLineItem` field-by-field, and the exception hierarchy. The exports below are what `from electronics_rfq_agent import ...` actually gives you, grepped from `src/electronics_rfq_agent/__init__.py`, not guessed:
| Export | What it is |
|---|---|
| `QuoteAgent` | Orchestrates parsing + ERP lookup + quote assembly. `run()` (async) and `run_sync()`. |
| `EpicorMCP`, `SAPMCP`, `OracleMCP`, `DynamicsMCP` | ERP connectors, one per supported system. All implement the same `search_parts` / `get_part` / `get_price` / `check_inventory` interface. |
| `MockERP` (from `electronics_rfq_agent.mcp.mock`) | In-memory backend with 200 realistic parts. No credentials, no network. |
| `Quote`, `QuoteLineItem`, `RFQLineItem`, `ERPPartResult`, `ERPConfig` | Pydantic v2 models for the quote, each line, the parsed RFQ line, raw ERP data, and connector config. |
| `ERPConnectionError`, `RFQParseError` | The two exceptions `QuoteAgent` can raise: connection/auth failures and unparseable documents. Per-line ERP failures don't raise; they land in `line.notes` instead. |
| `TelemetryCollector`, `TelemetryEvent` | Opt-in local telemetry (`telemetry=True` on `QuoteAgent`), counts and timings only, no RFQ content. |
## How it differs from the alternatives
| | Electronics RFQ Agent | Manual process | SAP Joule | Generic AI (ChatGPT) |
|---|---|---|---|---|
| Multi-ERP support | SAP + Epicor + Oracle + Dynamics | N/A | SAP-centric (Joule Studio can reach non-SAP sources via SAP Integration Suite) | No ERP access |
| Quote time (50 lines) | ~15s | 2-4 hours | Not publicly documented | N/A |
| Self-hostable | Yes | N/A | No (SAP BTP cloud service) | No |
| Data stays local | Yes | Yes | No | No |
| Open source | MIT | N/A | No | No |
| Dev mock backend | Yes | N/A | Not publicly documented | N/A |
| MCP compatible | Yes | N/A | Not publicly documented | No |
## ERP support
| ERP | Status | Connection | Docs |
|---|---|---|---|
| Epicor Kinetic | Supported | REST API | [Setup](docs/erp-setup/epicor.md) |
| SAP ECC / S/4HANA | Beta (manual install) | PyRFC (BAPI) | [Setup](docs/erp-setup/sap.md) |
| Oracle Cloud SCM | Supported | REST API | [Setup](docs/erp-setup/oracle.md) |
| Microsoft Dynamics 365 | Supported | Graph API | [Setup](docs/erp-setup/dynamics.md) |
| Mock backend | Built-in | In-memory | No config needed |
> **SAP note:** pyrfc requires the SAP NetWeaver RFC Library, which is not on PyPI and must be downloaded manually from SAP's support portal (S-user required). See [docs/erp-setup/sap.md](docs/erp-setup/sap.md) for step-by-step instructions.
## Benchmarks
Measured using the in-memory mock backend (200 realistic parts, no ERP system required). Run it yourself:
```bash
git clone https://github.com/RudrenduPaul/electronics-rfq-agent
cd electronics-rfq-agent
uv run python benchmarks/run.py
```
**ERP lookup latency (100 individual lookups, mock backend):**
| P50 | P99 | Mean |
|---|---|---|
| 0.00025ms | 0.0023ms | 0.000What people ask about electronics-rfq-agent
What is RudrenduPaul/electronics-rfq-agent?
+
RudrenduPaul/electronics-rfq-agent is mcp servers for the Claude AI ecosystem. AI quoting agent for electronics distributors. Parses RFQ documents (PDF, Excel, Word) with Claude, looks up every line item against your SAP, Epicor, Oracle, or Dynamics 365 ERP via MCP connectors, and returns a priced draft quote in seconds. Self-hosted, MIT licensed. It has 0 GitHub stars and its last recorded update is dated 2026-08-22.
How do I install electronics-rfq-agent?
+
You can install electronics-rfq-agent by cloning the repository (https://github.com/RudrenduPaul/electronics-rfq-agent) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is RudrenduPaul/electronics-rfq-agent safe to use?
+
Our security agent has analyzed RudrenduPaul/electronics-rfq-agent and assigned a Trust Score of 95/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains RudrenduPaul/electronics-rfq-agent?
+
RudrenduPaul/electronics-rfq-agent is maintained by RudrenduPaul. The last recorded GitHub activity is dated 2026-08-22, with 0 open issues.
Are there alternatives to electronics-rfq-agent?
+
Yes. On ClaudeWave you can browse similar mcp servers at /categories/mcp, sorted by popularity or recent activity.
Deploy electronics-rfq-agent to your cloud
Ship this repo to production in minutes. Each platform spins up its own environment with editable env vars.
Maintain this repo? Add a badge to your README
Drop the badge into your GitHub README to show it's tracked on ClaudeWave. Each badge links back to this page and reflects the live Trust Score.
[](https://claudewave.com/repo/rudrendupaul-electronics-rfq-agent)<a href="https://claudewave.com/repo/rudrendupaul-electronics-rfq-agent"><img src="https://claudewave.com/api/badge/rudrendupaul-electronics-rfq-agent" alt="Featured on ClaudeWave: RudrenduPaul/electronics-rfq-agent" width="320" height="64" /></a>More MCP Servers
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
An open-source AI agent that brings the power of Gemini directly into your terminal.
Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
The fastest path to AI-powered full stack observability, even for lean teams.
🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!