TenderAI
An MCP server that automates government and enterprise tender workflows, including RFP parsing, proposal generation, and compliance tracking. It provides 18 specialized tools for technical and financial proposal assembly, partner coordination, and hybrid search across past proposal archives.
README
TenderAI — MCP Server for Tender & Proposal Management
A production-ready Model Context Protocol server that automates government/enterprise tender workflows: RFP parsing, technical proposal writing, financial proposal assembly, partner coordination, and compliance tracking.
Features
- 18 MCP Tools across 5 domains: Document Intelligence, Technical Proposals, Financial Proposals, Partner Coordination, Past Proposal Indexing & Search
- Hybrid Search: FTS5 full-text keyword search + sqlite-vec vector similarity search with Reciprocal Rank Fusion (RRF)
- 5 Resource URI schemes for knowledge base access: past proposals, templates, vendors, company profile, standards
- 4 Workflow Prompts for end-to-end orchestration: tender analysis, executive summaries, partner checks, full proposal workflow
- AI-Powered: Uses Claude to parse RFPs, generate proposal sections, and produce compliance narratives
- Voyage AI Embeddings (optional): Semantic search over past proposals — finds similar projects even without exact keyword matches
- Document Generation: Professional DOCX proposals and XLSX BOM spreadsheets
- SQLite Database: Tracks RFPs, proposals, vendors, BOM items, partners, deliverables, and indexed past proposals
- OAuth 2.0: Built-in OAuth support for claude.ai integration (Dynamic Client Registration + PKCE, auto-approve)
Quick Start
Local Development (stdio)
# Clone and setup
cd tenders
python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
# Configure
cp .env.example .env
# Edit .env — set ANTHROPIC_API_KEY
# Run
python -m app.server
Claude Desktop / Claude Code Configuration
stdio (local):
{
"mcpServers": {
"tenderai": {
"command": "python",
"args": ["-m", "app.server"],
"cwd": "/path/to/tenders",
"env": {
"ANTHROPIC_API_KEY": "sk-ant-..."
}
}
}
}
HTTP (remote — Claude Code/Desktop):
{
"mcpServers": {
"tenderai": {
"type": "http",
"url": "https://tender.yfi.ae/mcp",
"headers": {
"Authorization": "Bearer <MCP_API_KEY>"
}
}
}
}
Claude.ai (OAuth 2.0):
Set OAUTH_ISSUER_URL=https://tender.yfi.ae in .env, then add https://tender.yfi.ae/mcp as an integration on claude.ai. OAuth flow completes automatically.
Production Deployment
sudo ./setup.sh tender.yfi.ae
# Edit /opt/tenderai/.env — set ANTHROPIC_API_KEY
sudo systemctl start tenderai
Tools
Document Intelligence
| Tool | Description |
|---|---|
parse_tender_rfp |
Parse PDF/DOCX RFP and extract structured data |
generate_compliance_matrix |
Generate compliance matrix DOCX for an RFP |
check_submission_deadline |
Check deadline and calculate milestones |
validate_document_completeness |
Validate proposal has all required sections |
Technical Proposals
| Tool | Description |
|---|---|
write_technical_section |
Write a single proposal section with AI |
build_full_technical_proposal |
Generate complete technical proposal DOCX |
generate_architecture_description |
Generate formal architecture narrative |
write_compliance_narrative |
Write compliance response for a requirement |
Financial Proposals
| Tool | Description |
|---|---|
ingest_vendor_quote |
Parse vendor quote and extract line items |
build_bom |
Build Bill of Materials from vendor quotes |
calculate_final_pricing |
Calculate final pricing with margins |
generate_financial_proposal |
Generate financial proposal DOCX + BOM XLSX |
Partner Coordination
| Tool | Description |
|---|---|
draft_partner_brief |
Draft technical requirements brief for partner |
create_nda_checklist |
Generate NDA checklist for partner engagement |
track_partner_deliverable |
Track expected deliverable from partner |
Past Proposal Indexing & Search
| Tool | Description |
|---|---|
index_past_proposal |
Parse + AI-summarize a past proposal folder into searchable index |
search_past_proposals |
Search indexed proposals — keyword, semantic, or hybrid mode |
list_indexed_proposals |
List all indexed proposals with aggregate stats |
Resources
| URI Pattern | Description |
|---|---|
proposals://past/{id} |
Past proposal content |
templates://{type} |
Proposal templates |
vendors://{name} |
Vendor profiles |
company://profile |
Company profile |
standards://{ref} |
Standards references |
Prompts
| Prompt | Description |
|---|---|
analyze_new_tender |
Full tender intake and go/no-go analysis |
write_executive_summary |
Tailored executive summary generation |
partner_suitability_check |
Evaluate partner fit for a tender |
full_proposal_workflow |
End-to-end proposal orchestration guide |
Knowledge Base
Populate these directories to improve AI-generated content:
data/
├── knowledge_base/
│ ├── company_profile/
│ │ └── profile.md # Company description, capabilities, differentiators
│ ├── templates/
│ │ ├── executive_summary.md
│ │ ├── technical_approach.md
│ │ └── ... # Section-specific templates
│ └── standards/
│ ├── iso27001.md
│ └── ... # Standards reference docs
├── past_proposals/
│ ├── tra_network_2024/
│ │ ├── Technical_Proposal.pdf # Your original submission files
│ │ ├── Cost_Sheet.xlsx # Financial data for pricing reference
│ │ └── _summary.md # Auto-generated by index_past_proposal
│ └── omantel_5g_2024/
│ └── ...
├── rfp_documents/ # Auto-populated by parse_tender_rfp
├── vendor_quotes/ # Vendor quote files
└── generated_proposals/ # Auto-populated output
Search Architecture
Past proposals can be indexed for fast retrieval:
Upload files → index_past_proposal → AI extracts metadata → stored in SQLite
├── FTS5 (keyword search, always on)
└── sqlite-vec (vector search, optional)
- FTS5: BM25 keyword ranking with porter stemming — sub-millisecond search
- Vector: Voyage AI embeddings (512-dim) stored in sqlite-vec — semantic similarity
- Hybrid: Both combined via Reciprocal Rank Fusion (RRF) for best results
- Set
VOYAGE_API_KEYin.envto enable vector search (200M free tokens from Voyage AI)
Backup
# Manual backup
./backup.sh /backups/tenderai 30
# Cron (daily at 2 AM)
0 2 * * * /opt/tenderai/backup.sh /backups/tenderai 30
Architecture
app/
├── server.py # Entry point — FastMCP init and wiring
├── config.py # Settings from .env
├── tools/
│ ├── document.py # 4 document intelligence tools
│ ├── technical.py # 4 technical proposal tools
│ ├── financial.py # 4 financial proposal tools
│ ├── partners.py # 3 partner coordination tools
│ └── indexing.py # 3 past proposal indexing & search tools
├── resources/
│ └── knowledge.py # 5 resource URI handlers
├── prompts/
│ └── workflows.py # 4 workflow prompts
├── db/
│ ├── schema.sql # SQLite schema (10 tables + FTS5 + triggers)
│ ├── database.py # Async database layer + sqlite-vec + OAuth CRUD
│ └── models.py # Pydantic models
├── services/
│ ├── llm.py # Anthropic SDK wrapper (15 prompt templates)
│ ├── parser.py # PDF/DOCX/XLSX parser
│ ├── embeddings.py # Voyage AI embedding service (optional)
│ └── docwriter.py # DOCX/XLSX generator
└── middleware/
├── auth.py # ASGI Bearer token auth (Claude Code/Desktop)
└── oauth.py # OAuth 2.0 provider (claude.ai)
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。