TenderAI

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.

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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_KEY in .env to 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)

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