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CSA Assessment Reports Server

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Enables Bob AI to access and analyze Cloudability Savings Automation assessment reports stored in SharePoint, allowing users to create pitch decks, analyze savi

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Описание

Enables Bob AI to access and analyze Cloudability Savings Automation assessment reports stored in SharePoint, allowing users to create pitch decks, analyze savings projections, and compare coverage through natural language.

README

An MCP (Model Context Protocol) server that gives Bob AI direct access to Cloudability Savings Automation (CSA) assessment reports stored in SharePoint. Sales and CSA teams can create pitch decks, analyze savings projections, and compare coverage—all through natural language.

What It Does

Instead of manually navigating SharePoint, downloading Excel files, and extracting data across 49 sheets, users simply ask Bob:

"Create a pitch deck for Apptio with 24-month savings projection"

Bob uses this MCP server to fetch, process, and return structured data in seconds.

graph LR
    A[User] -->|Natural Language| B[Bob AI]
    B -->|MCP Protocol| C[MCP Server]
    C -->|REST API| D[Backend Service]
    D -->|OAuth 2.0| E[SharePoint]

    style A fill:#4a9eff,color:#fff
    style B fill:#ff8c42,color:#fff
    style C fill:#a855f7,color:#fff
    style D fill:#22c55e,color:#fff
    style E fill:#eab308,color:#000

SharePoint Data Layout

Assessment reports are distributed across 4 SharePoint sites. The primary site holds master reports (consolidated portfolio data), while the other 3 sites share the volume of individual assessment reports across all organizations and payer accounts.

graph TB
    subgraph "SharePoint Online — 4 Sites"
        S1[🗂️ Primary Site: CSAReporting<br/>Master Reports only<br/>Portfolio-wide consolidated data]

        S2[📄 Site 2: CSAReporting 2<br/>Assessment Reports<br/>About one-third of all payer accounts]
        S3[📄 Site 3: CSAReporting 3<br/>Assessment Reports<br/>About one-third of all payer accounts]
        S4[📄 Site 4: CSAReporting 4<br/>Assessment Reports<br/>About one-third of all payer accounts]
    end

    S1 ~~~ S2
    S2 ~~~ S3
    S3 ~~~ S4

    style S1 fill:#eab308,color:#000
    style S2 fill:#4a9eff,color:#fff
    style S3 fill:#4a9eff,color:#fff
    style S4 fill:#4a9eff,color:#fff

Each assessment report is an Excel file with 49 sheets containing compute usage, savings plans, reserved instances, planning models, and projections. Reports are split across 3 sites to manage SharePoint's file volume limits.

Business Value

Time Comparison: Manual vs Bob-Assisted

Step Manual With Bob
Find the right report 8-18 min (navigate SharePoint, guess which site) 10 sec (parallel search across all sites)
Download & open file 7 min (50-100MB Excel) 30 sec (server-side download, cached after first access)
Locate data in 49 sheets 10-15 min 15 sec (structured extraction)
Extract & format metrics 15-20 min 15 sec
Create deliverable 10-15 min 60 sec (LLM streaming + file generation)
Total 30-70 min ~2-3 min
graph LR
    subgraph BEFORE[" Manual Process "]
        B[Navigate SharePoint<br/>Download Excel<br/>Find data in 49 sheets<br/>Copy to PowerPoint<br/><br/>30-70 minutes]
    end

    subgraph AFTER[" Bob-Assisted Process "]
        A[Ask Bob in natural language<br/>Auto-fetch, extract, generate<br/><br/>2-3 minutes]
    end

    BEFORE -->|~95x faster| AFTER

    style B fill:#e53e3e,color:#fff
    style A fill:#38a169,color:#fff

Architecture

The project uses a two-service architecture to resolve Pydantic v1/v2 dependency conflicts:

graph TB
    subgraph "AWS Cloud"
        subgraph "API Gateway"
            REST[REST API<br/>Lambda Backend]
            HTTP[HTTP API<br/>Fargate MCP]
        end

        subgraph "Compute"
            LAMBDA[Lambda Function<br/>Backend Service<br/>Pydantic v1 + FastAPI]
            FARGATE[Fargate Task<br/>MCP Server<br/>Pydantic v2 + FastMCP]
        end

        subgraph "Networking"
            VPCLINK[VPC Link]
            CLOUDMAP[Cloud Map<br/>Service Discovery]
        end

        subgraph "Data"
            SP[SharePoint Online<br/>4 Sites]
        end
    end

    REST --> LAMBDA
    HTTP --> VPCLINK
    VPCLINK --> CLOUDMAP
    CLOUDMAP --> FARGATE
    FARGATE -->|calls| REST
    LAMBDA --> SP

    style LAMBDA fill:#22c55e,color:#fff
    style FARGATE fill:#a855f7,color:#fff
    style SP fill:#eab308,color:#000
Component Runtime Purpose
Backend Service Lambda (ARM64) SharePoint integration, Excel processing, caching
MCP Server Fargate (ARM64) MCP protocol handling, tool definitions, LLM communication

MCP Tools

1. list_assessment_reports

Find available reports by organization and time period.

{
  "org_id": "113798",
  "payer_account_id": "788915724807",
  "year": 2026,
  "month": 5
}

2. get_assessment_summary_metrics

Extract executive summary with key metrics, savings opportunities, and recommendations.

{
  "file_id": "01XXXXXXXXXXXX"
}

3. get_assessment_sheet

Retrieve any of the 49 sheets with pagination support (up to 5000 rows/page).

{
  "file_id": "01XXXXXXXXXXXX",
  "sheet_name": "compute_usage.csv",
  "page": 1,
  "page_size": 1000,
  "format": "csv"
}

4. get_assessment_sheet_names

List all available sheet names in a report before fetching data.

{
  "file_id": "01XXXXXXXXXXXX"
}

5. get_master_report_summary

Get consolidated data across multiple payer accounts.

{
  "file_id": "01XXXXXXXXXXXX",
  "category": "Cat5 EC2>$40M"
}

6. parse_sharepoint_url

Parse SharePoint URLs (sharing links or direct) to extract file IDs for other tools.

{
  "url": "https://company.sharepoint.com/sites/site/file.xlsx",
  "return_type": "metadata"
}

Bob Integration

This MCP server integrates with Bob (IBM's AI assistant) to provide natural language access to CSA assessment data. Bob uses a dedicated skill (csa-assessments-analyzer) that connects to this MCP server and automates the full workflow from data retrieval to deliverable generation.

sequenceDiagram
    participant User
    participant Bob
    participant MCP as MCP Server
    participant BE as Backend
    participant SP as SharePoint

    User->>Bob: "Create pitch deck for Apptio"
    Bob->>MCP: list_assessment_reports(org_id, year, month)
    MCP->>BE: POST /api/list_assessment_reports
    BE->>SP: Search across 3 assessment sites (parallel)
    SP-->>BE: Found report in Site 2
    BE-->>MCP: Report metadata with file_id
    MCP-->>Bob: Available reports

    Bob->>MCP: get_assessment_summary_metrics(file_id)
    MCP->>BE: POST /api/get_assessment_summary_metrics
    BE->>SP: Download & cache Excel (4hr TTL)
    BE-->>MCP: Structured metrics JSON
    MCP-->>Bob: Summary data

    Bob-->>User: Formatted pitch deck with projections

What Bob Can Generate

Using the data from this MCP server, Bob produces:

Output Format Description
Executive Brief .docx Professional Word document with savings analysis
Pitch Deck .pptx PowerPoint presentation with projections
Interactive Dashboard .html Chart.js-powered dashboard with calculators
Sales Pitch Inline Structured narrative with YoY breakdowns

Example Prompts

  • "Create a pitch deck for Apptio with 24-month savings projection"
  • "Show flexibility analysis for a 36-month CSA engagement"
  • "Compare current vs projected coverage for org 113798"
  • "What sheets are available in this report?"
  • "Generate an interactive dashboard for IBM"
  • "What are the non-EC2 savings opportunities?"

Bob Workflow

flowchart LR
    A[User Request] --> B[Find Report]
    B --> C[Extract Metrics]
    C --> D{Output Type?}
    D -->|Analysis| E[Formatted Summary]
    D -->|Pitch Deck| F[.docx + .pptx]
    D -->|Dashboard| G[Interactive .html]

    style A fill:#4a9eff,color:#fff
    style D fill:#ff8c42,color:#fff
    style F fill:#38a169,color:#fff
    style G fill:#38a169,color:#fff

MCP Server Connection

Bob connects to the MCP server via the configured endpoint:

{
  "mcpServers": {
    "csa-assessments-prod": {
      "type": "streamable-http",
      "url": "https://<http-api-id>.execute-api.us-west-2.amazonaws.com/mcp"
    }
  }
}

AWS Deployment

Infrastructure Overview

┌─────────────────────────────────────────────────────┐
│ API Gateway (REST)          API Gateway (HTTP)       │
│ └─ /api/{proxy+}           └─ /mcp/{proxy+}        │
│    → Lambda                    → VPC Link           │
│                                   → Cloud Map       │
│                                      → Fargate      │
├─────────────────────────────────────────────────────┤
│ VPC (Private Subnets)                               │
│ ┌─────────────┐  ┌──────────────┐  ┌───────────┐  │
│ │ Lambda ENI  │  │ Fargate Task │  │ VPC Link  │  │
│ │ (Backend)   │  │ (MCP Server) │  │ ENIs      │  │
│ └─────────────┘  └──────────────┘  └───────────┘  │
├─────────────────────────────────────────────────────┤
│ Supporting Services                                  │
│ • Cloud Map (service discovery with SRV records)    │
│ • ECR (container images)                            │
│ • CloudWatch Logs                                   │
│ • S3 (file cache bucket)                            │
└─────────────────────────────────────────────────────┘

Key Configuration

Resource Detail
Lambda Python 3.13, ARM64, 3008MB, 300s timeout
Fargate 256 CPU, 512MB, ARM64, Streamable HTTP transport
VPC Link Private subnets, Cloud Map service discovery
Cloud Map A + SRV records for port-aware routing
Cache S3 bucket with 1-day expiration

Deployment

# Prerequisites: AWS credentials, CodeArtifact access

# 1. Prepare dependencies (Linux ARM64 wheels)
./scripts/prepare-deployment.sh

# 2. Build and push MCP server container
./scripts/build-and-push-ecr.sh

# 3. Deploy with Serverless Framework
serverless deploy --stage dev1 --region us-west-2

# 4. Force ECS task to pull latest image
aws ecs update-service \
  --cluster csa-assessments-mcp-dev1-cluster \
  --service csa-assessments-mcp-dev1-mcp-service \
  --force-new-deployment

Endpoints

After deployment:

  • Backend API: https://<rest-api-id>.execute-api.us-west-2.amazonaws.com/<stage>/api/health
  • MCP Server: https://<http-api-id>.execute-api.us-west-2.amazonaws.com/mcp

Multi-Site SharePoint Architecture

The backend dynamically routes requests across 4 SharePoint sites:

graph TB
    BE[Backend Service<br/>Dynamic Router]

    BE -->|Master Reports| S1[Site 1: CSAReporting<br/>Portfolio-wide data]
    BE -->|Parallel Search| S2[Site 2: CSAReporting 2<br/>About one-third of assessment reports]
    BE -->|Parallel Search| S3[Site 3: CSAReporting 3<br/>About one-third of assessment reports]
    BE -->|Parallel Search| S4[Site 4: CSAReporting 4<br/>About one-third of assessment reports]

    style BE fill:#22c55e,color:#fff
    style S1 fill:#eab308,color:#000
    style S2 fill:#4a9eff,color:#fff
    style S3 fill:#4a9eff,color:#fff
    style S4 fill:#4a9eff,color:#fff
  • Users don't need to know which site holds their data
  • Parallel queries across all 3 assessment sites
  • Reports distributed across sites to manage SharePoint file volume limits
  • Automatic caching with 4-hour TTL
  • Resilient — if one site is unavailable, others continue working

Local Development

Prerequisites

  • Python 3.13+
  • uv package manager
  • Azure AD credentials for SharePoint
  • Access to CloudWiry CodeArtifact

Setup

# Backend (Pydantic v1)
cd backend && uv sync && cd ..

# MCP Server (Pydantic v2)
cd mcp && uv sync && cd ..

# Configure environment
cp .env.example .env  # Edit with your credentials

# Start both services
./start_all.sh

VSCode

Open the workspace file for automatic interpreter switching between the two projects:

code assessments-mcp-server.code-workspace

Project Structure

assessments-mcp-server/
├── backend/                     # Backend service (Lambda)
│   ├── pyproject.toml          # Pydantic v1 dependencies
│   ├── backend_service.py      # FastAPI application
│   ├── lambda_handler.py       # Lambda entry point (Mangum)
│   ├── settings.py             # Configuration
│   └── src/
│       ├── models/             # Input/output/internal models
│       ├── sharepoint/         # Client, discovery, cache
│       ├── processing/         # Excel, CSV, summary extraction
│       ├── services/           # Business logic
│       └── utils/              # Filename parsing, validators
├── mcp/                        # MCP server (Fargate)
│   ├── pyproject.toml          # Pydantic v2 dependencies
│   ├── mcp_server.py           # FastMCP tool definitions
│   ├── mcp_models.py           # Input validation models
│   └── Dockerfile              # Container image
├── scripts/                    # Deployment scripts
├── serverless.yml              # Infrastructure as Code
└── start_all.sh                # Local dev launcher

Error Handling

All tools return structured error responses:

{
  "error": "Report not found: invalid-id",
  "tool": "get_assessment_summary_metrics",
  "hint": "Use list_assessment_reports to find available reports."
}

License

Internal use only — CloudWiry/Apptio

Version History

  • 0.1.0 (2026-06-16) - Initial implementation
    • 6 MCP tools for assessment report operations
    • Multi-site SharePoint integration with dynamic routing
    • AWS deployment on Lambda + Fargate
    • Bob AI integration via Streamable HTTP transport

from github.com/sKumaraguru/apptio-bobathon-2026-finbob-mcp-server

Установка CSA Assessment Reports Server

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/sKumaraguru/apptio-bobathon-2026-finbob-mcp-server

FAQ

CSA Assessment Reports Server MCP бесплатный?

Да, CSA Assessment Reports Server MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для CSA Assessment Reports Server?

Нет, CSA Assessment Reports Server работает без API-ключей и переменных окружения.

CSA Assessment Reports Server — hosted или self-hosted?

Доступен hosted-вариант: Unyly запускает сервер в облаке, локальная установка не обязательна.

Как установить CSA Assessment Reports Server в Claude Desktop, Claude Code или Cursor?

Открой CSA Assessment Reports Server на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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