Allows GitHub Copilot to manage student leave requests and approvals through MCP tools such as listing students, getting student details, and approving leave requests.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-student-leave-copilot-ab Show me pending leave requests"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A lightweight MCP server in Node.js, integrated with Github Copilot, to manage student leave requests and approvals.
Related MCP server: MCP Leave Manager Server
mkdir mcp-student-leave-copilot-ab
cd mcp-student-leave-copilot-ab
npm init -y
npm i @modelcontextprotocol/sdk zod
node server.js
Student Leave MCP server (stdio) and Github Copilot agent ready!
{
"type": "module",
"scripts": {
"start": "node server.js"
}
}Github Copilot looks for MCP servers in its mcp.json
/Users/abhijeetgiram/Library/Application Support/Code/User/mcp.json
{
"servers": {
"mcp-student-leave-copilot-ab": {
"command": "node",
"args": [
"/Users/abhijeetgiram/Workspace/Personal/mcp-student-leave-copilot-ab/server.js"
],
"alwaysAllow": ["list_students", "get_student_details", "approve_leave"],
"disabled": false
}
}
}In a Github Copilot chat, just ask things like:
“Show me the student list.” → Github Copilot calls list_students
“Get details of E002.” → Github Copilot calls get_student_details with { id: "S002" }
“Approve leave L1002 for E002.” → Github Copilot calls approve_leave with { studentId: "S002", leaveId: "L2002" }
It’s a new open standard by Anthropic that defines how AI models, tools, and apps can talk to each other.
Think of it like a “plugin protocol” for AI assistants.
MCP Server = Your code (like the student leave manager we built).
MCP Client = Something that talks to the server (Github Copilot, Cline, Claude, or any AI agent).
They communicate over:
stdio (local processes, great for Github Copilot in VS Code)
or HTTP/WebSocket (remote deployment, so web apps / React can call it).
MCP turns your Node.js script into a “tool plugin” for AI assistants.
{ "students": [ { "id": "S001", "name": "Rahul Sharma", "class": "10A", "leaveBalance": 12 }, { "id": "S002", "name": "Priya Desai", "class": "9B", "leaveBalance": 15 }, { "id": "S003", "name": "Ananya Gupta", "class": "11C", "leaveBalance": 10 } ], "leaves": [ { "id": "L2001", "studentId": "S001", "from": "2025-09-01", "to": "2025-09-02", "days": 2, "status": "pending" }, { "id": "L2002", "studentId": "S002", "from": "2025-09-05", "to": "2025-09-07", "days": 3, "status": "pending" } ] }
pwd
/Users/abhijeetgiram/Workspace/Personal/mcp-student-leave-copilot-ab
/Users/abhijeetgiram/Library/Application Support/Code/User/settings.json
To create a file on Mac using the command line, use the touch command. For example, to create a file named
touch mcp.json
You can also create and edit a file using nano:
nano mcp.json
Approve a pending leave and update balance.
| Name | Required | Description | Default |
|---|---|---|---|
| leaveId | Yes | ||
| studentId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It mentions that the tool updates the balance, which is a side effect, but does not specify permissions required, reversibility, error conditions, or behavior for invalid or already-approved leaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no fluff, but it is too brief to convey necessary context. The structure is efficient, though the content is minimal and could be expanded to include important details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two required parameters, no output schema, and no annotations, this description is insufficient for a mutation tool. It does not explain the balance update mechanism, expected return values, or any prerequisites, leaving significant gaps for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the meaning or role of leaveId or studentId. The parameter names are self-explanatory in the schema, but the description adds no semantic value beyond the schema, failing to compensate for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: approving a pending leave and updating the balance. It uses a specific verb ('approve') with a resource ('pending leave'), which distinguishes it from sibling tools that list or retrieve student information.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is provided on when to use this tool versus alternatives, nor are there any prerequisites or exclusions. The description implies usage for approving pending leaves but does not explain when to prefer this over list_students or get_student_details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return one student and their leave requests.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Student ID, e.g. S001 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only states the happy-path return (student and leave requests) but does not mention error handling, whether all leave requests are included or only specific ones, or any authentication requirements. This leaves significant gaps in behavioral transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence (six words) that efficiently conveys the core function. It is front-loaded and contains no fluff, earning full marks for conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity and a well-described parameter schema, the description is partially complete. However, the lack of an output schema means the description should elaborate on the return shape (e.g., what fields are included in the student object and the leave requests), and it does not mention alternative tools or edge cases, so it falls short of full completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the 'id' parameter with a clear description and example ('Student ID, e.g. S001'), and coverage is 100%. The description does not add extra semantic detail about the parameter beyond what the schema already provides, so it meets the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('Return') and resource ('one student and their leave requests'). It distinguishes itself from sibling tools like list_students (which lists multiple) and approve_leave (which modifies leave status). The title reinforces the 'by ID' aspect.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool should be used when fetching a single student's details, but it does not explicitly state when to use it versus alternatives. No exclusions or alternative tool mentions are provided, leaving usage guidance at an implied level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return all students with remaining leave balance.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states the action ('Return all students') but does not clarify whether 'with remaining leave balance' filters the result set or simply includes the balance field. It also omits details on ordering, pagination, or response format, leaving room for misinterpretation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that immediately conveys the action and resource. It is concise, front-loaded with the verb 'Return,' and contains no wasteful phrasing, allowing an agent to parse it quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless read tool, the description covers the core purpose but leaves a key ambiguity about whether 'with remaining leave balance' filters the returned students or is just an included attribute. With no output schema or annotations to clarify behavior, an agent may not know the exact response structure, so a mid-range score is warranted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
This tool has zero parameters, so the input schema provides complete coverage (vacuously 100%). The description does not need to document parameter syntax, making the baseline of 4 appropriate. No additional parameter-related information is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Return' and identifies the resource 'all students' with a qualifying phrase 'with remaining leave balance.' This clearly distinguishes it from sibling tools like get_student_details (likely a single-student lookup) and approve_leave (a mutation). Minor ambiguity exists about whether the qualifier filters students or includes a field, but the core purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention get_student_details for individual lookups or approve_leave for leave approvals, nor any exclusions or prerequisites. Agents must infer usage solely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0approve_leaveget_student_detailslist_studentsScored across 3 tools
Each tool has a clearly distinct purpose: listing all students, fetching a single student's details, and approving a leave. There is no overlap or ambiguity between them.
All tool names follow a consistent verb_noun snake_case pattern: list_students, get_student_details, approve_leave. The naming style is uniform and predictable.
Three tools is well-scoped for a focused student leave copilot, each serving a distinct function without redundancy. The count is within the ideal 3-15 range.
The core workflow of viewing students and approving leave is covered, but there is no reject or batch approval capability. Minor gaps exist, but they are workaroundable by iterating through students.
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