Appraisal Tracker MCP

Appraisal Tracker MCP

Enables tracking annual performance appraisal by logging professional achievements as structured Markdown notes to an Obsidian vault through natural language conversation with VS Code Copilot.

Category
Visit Server

README

Appraisal Tracker MCP

A local MCP (Model Context Protocol) server that connects to VS Code Copilot and helps you track your annual performance appraisal by logging evidence directly into an Obsidian vault — using natural language conversation.

Instead of manually filling spreadsheets or forgetting what you accomplished, you just tell Copilot what you did and it handles the rest: asks follow-up questions, maps the achievement to your appraisal goals, attaches screenshots, and writes structured Markdown notes to your vault.


What it does

  • Log professional achievements conversationally — no forms, no structure needed
  • Automatically maps evidence to appraisal goals by rubric
  • Attaches screenshots or images to evidence notes
  • Lists all logged evidences with filters
  • Generates a full appraisal evidence report
  • Generates a mid-year summary note (English, Spanish, or both)
  • Generates an actionable game plan for completing remaining goals

Tools available

Tool What it does
add_evidence Save an achievement and link it to appraisal goals
get_appraisal Look up all goals for a level
list_evidences List logged evidences with optional filters
generate_report Generate a full Markdown report organized by rubric and goal
generate_summary Generate a mid-year summary note (English, Spanish, or both)
generate_game_plan Generate a prioritized game plan for remaining goals
update_evidence_image Attach or add an image to an existing evidence note

Requirements

  • Node.js v18+ — recommended to use nvm
  • VS Code with the GitHub Copilot extension
  • Obsidian (optional but recommended — the vault is just a folder of Markdown files)

Setup

1. Clone the repo

git clone https://github.com/your-username/appraisal-tracker-mcp.git
cd appraisal-tracker-mcp

2. Install dependencies

node --version  # make sure you're on v18+
npm install

3. Configure environment variables

Copy the example env file and edit it:

cp .env.example .env

Open .env and set your paths:

OBSIDIAN_VAULT_PATH=/Users/yourname/Documents/YourVault
TRACKER_FOLDER=Appraisal Tracker
DEFAULT_LEVEL=lead_consultant
  • OBSIDIAN_VAULT_PATH — the root folder of your Obsidian vault (or any folder where you want the notes saved)
  • TRACKER_FOLDER — the subfolder inside the vault where all tracker files will be created
  • DEFAULT_LEVEL — your appraisal level. The server uses this automatically so you never have to specify it in conversation. Change it to match your level (e.g., senior_consultant, principal_consultant)

4. Build the project

node --version          # confirm Node 18+
npm run build           # compiles TypeScript to dist/

If you're using nvm and the wrong Node version is active, run:

nvm use 20
npm run build

5. Configure VS Code Copilot

Create a .vscode/mcp.json file in this project (or in any workspace you want to use the tracker from):

{
  "servers": {
    "appraisal-tracker": {
      "type": "stdio",
      "command": "/absolute/path/to/node",
      "args": ["/absolute/path/to/appraisal-tracker-mcp/dist/index.js"],
      "env": {
        "OBSIDIAN_VAULT_PATH": "/Users/yourname/Documents/YourVault",
        "TRACKER_FOLDER": "Appraisal Tracker",
        "DEFAULT_LEVEL": "lead_consultant"
      }
    }
  }
}

Important: Use the absolute path to your Node binary — ${workspaceFolder} does not work here.

To find your Node path:

which node
# or if using nvm:
nvm which 20

6. Start the MCP server in VS Code

  1. Open the Command Palette (Cmd+Shift+P / Ctrl+Shift+P)
  2. Search for "MCP: List Servers"
  3. Find appraisal-tracker and click Start Server
  4. Open Copilot Chat, switch to Agent mode
  5. You should see the tools listed — you're ready to go

Folder structure created in your vault

YourVault/
└── Appraisal Tracker/
    ├── attachments/          ← screenshots and images go here
    ├── evidences/
    │   └── 2026/
    │       └── lead_consultant/
    │           ├── ev_20260427_001.md
    │           └── ev_20260518_001.md
    ├── reports/              ← generated by generate_report
    ├── 2026 Mid-Year Summary.md
    ├── 2026 Resumen Mid-Year.md
    └── 2026 Game Plan.md

How to use it

Open Copilot in Agent mode and just talk to it naturally:

Logging an achievement:

"I got my Azure AI Fundamentals certification today"

Copilot will ask follow-up questions (date, people involved, outcome, do you have a screenshot?) and then save the evidence note automatically.

Attaching a screenshot:

Save your screenshot to YourVault/Appraisal Tracker/attachments/ first, then tell Copilot: "Add the latest screenshot to that evidence"

Generating a summary:

"Generate my mid-year summary in both English and Spanish"

Generating a game plan:

"Generate my game plan. I'm currently working on: a dashboard for banking users, a skill meter tool, and studying for the AI-102 certification"

Listing what you've logged:

"Show me all my evidences for this year"


Adding support for other appraisal levels

Currently includes goals for lead_consultant. To add another level (e.g., Senior Consultant, Principal, etc.):

1. Create the appraisal JSON file

Add a new file at src/data/appraisals/senior_consultant.json (use lowercase and underscores for the filename).

The file must follow this structure:

{
  "level": "senior_consultant",
  "displayName": "Senior Consultant",
  "rubrics": [
    {
      "id": "develop_business",
      "name": "Develop your business",
      "goals": [
        {
          "id": 1,
          "description": "Description of goal 1 exactly as it appears in the appraisal rubric"
        },
        {
          "id": 2,
          "description": "Description of goal 2"
        }
      ]
    },
    {
      "id": "develop_yourself",
      "name": "Develop yourself",
      "goals": [
        {
          "id": 7,
          "description": "Obtain a technical certification aligned to your technology stack"
        }
      ]
    },
    {
      "id": "manage_business",
      "name": "Manage your business",
      "goals": []
    },
    {
      "id": "support_customers",
      "name": "Support your customers",
      "goals": []
    }
  ]
}

Rules:

  • level must match the filename exactly (e.g., senior_consultant → senior_consultant.json)
  • id inside each rubric must be one of: develop_business, develop_yourself, manage_business, support_customers
  • Goal id can be a number (1, 2) or a string for sub-goals ("6a", "6b")
  • Copy the goal descriptions directly from the official appraisal document so they match exactly

2. Set your level in .env and mcp.json

Update DEFAULT_LEVEL in your .env file:

DEFAULT_LEVEL=senior_consultant

And in .vscode/mcp.json under env:

"DEFAULT_LEVEL": "senior_consultant"

This is the level the server will use automatically — you never have to specify it in conversation.

3. Rebuild the project

npm run build

4. Restart the MCP server in VS Code

Open MCP: List Servers from the Command Palette and restart appraisal-tracker.

4. Use it

Now you can tell Copilot:

"Add evidence for level senior_consultant — I led a client demo today"

Or generate a summary for that level:

"Generate my mid-year summary for senior_consultant"

You can have multiple levels active at the same time — each one stores its evidences in its own subfolder inside the vault (evidences/2026/senior_consultant/).


Appraisal goal structure

Goals are organized in 4 rubrics:

Rubric Description
develop_business Business development, mentoring, case studies, sales support
develop_yourself Certifications, learning, communication, technical growth
manage_business Compliance, utilization, processes, professional development
support_customers Delivery quality, client relationships, team support

Customizing Copilot behavior

The file .github/copilot-instructions.md controls how Copilot behaves as an agent — what questions it asks before saving evidence, the tone it uses, and how it maps goals.

Edit this file to adjust the behavior for your workflow.


Tech stack

  • TypeScript + Node.js
  • @modelcontextprotocol/sdk — MCP server implementation
  • zod — input validation
  • dotenv — environment configuration
  • Plain Markdown files as storage (no database)

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured