How do I use LeaveManager?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LeaveManager What's my remaining annual leave balance?" 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.

LeaveManager

by DhanyaHegdek

Overview Schema Related Servers Score Discussions

Python

MCP Server Setup Guide (Windows) — Leave Management Example

This README documents the full process of building a local MCP (Model Context Protocol) server in Python with uv, testing it with MCP Inspector, and connecting it to Claude Desktop on Windows — including the gotchas that come up along the way.

1. Project Setup

cd Desktop\MCP
mkdir my-mcp-server
cd my-mcp-server
uv init .
uv add "mcp[cli]"

Gotcha: Installing mcp[cli] with plain pip install mcp[cli] puts it in your global Python site-packages — NOT in the project's own uv virtual environment. uv run only sees packages installed via uv add (or uv pip install) inside the project. If you see:
Error: typer is required. Install with 'pip install mcp[cli]'
even after installing it, this is almost always the cause. Fix: run uv add "mcp[cli]" from inside the project folder.

Related MCP server: leave_manager

2. Example `main.py`

Build your server using FastMCP from the mcp package: create an mcp = FastMCP("YourServerName") instance, define functions decorated with @mcp.tool() for actions the AI can call (each with a clear docstring describing what it does — Claude uses this to decide when to call it), and optionally @mcp.resource("scheme://{param}") for read-only data resources. End the file with:

if __name__ == "__main__":
    mcp.run()

For this walkthrough, the example server was a simple in-memory "LeaveManager" with three tools (get_leave_balance, apply_leave, get_leave_history) and one resource (greeting://{name}).

stdout is reserved for the JSON-RPC protocol — any stray print() corrupts the stream and causes cryptic JSON parse errors downstream. Use logging configured to stderr if you need debug output.

3. Test with MCP Inspector (recommended before touching Claude Desktop)

MCP Inspector is a browser-based tool for calling your tools directly — no AI, no Claude Desktop needed. Great for confirming your server actually works before wiring it into anything else.

uv run mcp dev main.py

This opens a local page (usually http://localhost:6274). Click Connect, then go to the Tools tab → List Tools → pick a tool → fill params → run it and check the output.

If Connect spams SyntaxError: Unexpected token ... is not valid JSON in the History/Notifications panel, it usually means the underlying command errored out in plain text (e.g. the same "typer is required" issue above) instead of returning JSON. Read the actual error text hiding in the syntax error message — it tells you what broke.

4. Connecting to Claude Desktop

4a. If Claude Desktop isn't installed yet

Download from https://claude.ai/download, install, and launch it at least once (finish sign-in) before doing anything else.

4b. Finding the right config file

This is the step that varies the most and caused the most confusion:

Standard installs usually use: %APPDATA%\Claude\claude_desktop_config.json (i.e. C:\Users\<you>\AppData\Roaming\Claude\claude_desktop_config.json)
Packaged/Store (MSIX) installs — recognizable by a path containing AppData\Local\Packages\Claude_<random-id>\... — use a virtualized config location instead, e.g.: ...\Local\Packages\Claude_<id>\LocalCache\Roaming\Claude\claude_desktop_config.json Windows silently redirects the app's "AppData\Roaming\Claude" reads/writes to this Packages folder, so editing the plain %APPDATA%\Claude copy does nothing for this install type.
Easiest way to find the right one: open Claude Desktop → Settings → Developer (under "Desktop app" section) → Local MCP servers → click Edit Config. This always opens the file the app actually reads, regardless of install type.

Dead end to avoid: Settings → Connectors → Add custom connector looks like the obvious place to add a server, but it's only for remote MCP servers (ones reachable by a URL, e.g. a hosted server on the internet). It has no field for a local command like uv run main.py, so don't waste time there for a local stdio server — go to Developer → Local MCP servers instead.

4c. The config format

The file may already contain other keys (preferences, Cowork settings, etc. on newer builds). Just add mcpServers as a new top-level key — don't delete anything else:

{
  "mcpServers": {
    "leave-manager": {
      "command": "uv",
      "args": [
        "--directory",
        "C:\\Users\\<you>\\Desktop\\MCP\\my-mcp-server",
        "run",
        "main.py"
      ]
    }
  }
}

If "command": "uv" isn't found (Claude Desktop doesn't always inherit your terminal's PATH), use the full path instead, e.g.: C:\\Users\\<you>\\AppData\\Local\\Programs\\Python\\Python314\\Scripts\\uv.exe

4d. Restart and verify

Save the config file.
Fully quit Claude Desktop — right-click its icon in the system tray (bottom-right, near the clock) → Quit/Exit. Closing the window alone is not enough.
Reopen Claude Desktop.
Go to Settings → Developer → Local MCP servers — your server should show up with a green "running" badge.
Start a new chat and ask a natural question, e.g.: "How many leave days does E001 have left?" Claude should say "Loaded tools, used <your-server-name> integration" and answer using your tool's actual return value.

5. Quick troubleshooting checklist

Symptom	Likely cause	Fix
`Error: typer is required...`	`mcp[cli]` installed globally, not in project venv	`uv add "mcp[cli]"` inside the project folder
`Claude app not found` from `mcp install`	Claude Desktop not installed, or `mcp install`'s auto-detect is unreliable	Install the app, or skip `mcp install` and edit the config file directly
Inspector shows repeated JSON parse errors	Server process is printing plain text instead of JSON (crash message, or a stray `print()`)	Check the error text embedded in the parse error; fix the underlying issue
Settings → Developer → "No servers added" after editing config	Edited the wrong config file, or `mcpServers` key missing/malformed	Use Settings → Developer → Edit Config to find the actual file in use; verify JSON is valid
Server shows up but Claude never calls it	Ambiguous tool descriptions, or server crashed silently	Check "View Logs" next to the server entry in Settings → Developer

6. Useful commands reference

uv init .                      # create a new uv project
uv add "mcp[cli]"              # add MCP with CLI/dev tools as a dependency
uv run mcp dev main.py          # launch MCP Inspector against your server
uv run mcp install main.py      # (optional) attempt auto-install into Claude Desktop
uv run main.py                  # run the server standalone (useful for checking for crashes)

Available Tools

3 tools

apply_leaveB

Apply leave for specific dates (e.g., ["2025-04-17", "2025-05-01"])

ParametersJSON Schema

Name	Required	Description	Default
`employee_id`	Yes
`leave_dates`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, and the description covers only the action; it misses behavioral traits like side effects, authorization needs, or result handling expected from a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence is concise and front-loaded, but the structure is minimal; could include more detail without bloat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with output schema, the description is too sparse: no return info, no lifecycle or effect beyond the example.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema coverage, the description must explain parameters; it provides an example for leave_dates but omits any explanation for employee_id.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('apply leave') and the resource ('specific dates'), distinguishing it from sibling tools like get_leave_balance and get_leave_history.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose implies usage for applying leave, but the description does not explicitly contrast with alternatives or provide conditions for use vs. not use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_leave_balanceB

Check how many leave days are left for the employee

ParametersJSON Schema

Name	Required	Description	Default
`employee_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description must convey behavioral traits. It only implies a read-only operation ('check') but omits details like permissions, side effects (e.g., whether the operation is logged), or the format of the result. With output schema existing, the return format is covered, but the description itself adds minimal transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence of 8 words. Every word contributes to the purpose, and there is no redundancy or filler. Ideal conciseness for a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, output schema exists), the description is functionally adequate but lacks context such as what specific leave-related data is returned (e.g., total, used, or remaining days) and when to prefer this over siblings. Output schema may fill some gaps, but the description alone leaves room for interpretation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The only parameter 'employee_id' has no schema description (0% coverage) and the tool description merely says 'for the employee', offering no additional semantics about expected format, domain, or validation. The description does not compensate for the missing parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Check') and the resource ('how many leave days are left for the employee'), making the tool's purpose unambiguous. It also naturally distinguishes from sibling tools 'apply_leave' (applies leave) and 'get_leave_history' (retrieves history) by focusing on balance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is given on when to use this tool versus its siblings. The description implies checking current balance but does not explain that 'get_leave_history' is for past usage or that 'apply_leave' is for deductions. This leaves the agent without decision criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_leave_historyC

Get leave history for the employee

ParametersJSON Schema

Name	Required	Description	Default
`employee_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.6/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavioral traits. It does not mention that this is a read operation, nor does it describe the output structure, pagination, or scope of 'leave history'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (one sentence) and front-loaded, but it sacrifices essential detail for brevity. It is not overly verbose, but it is under-specified.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite the presence of an output schema, the description lacks contextual completeness. It fails to clarify the input parameter, the difference from sibling tools, or the behavior of the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage for employee_id, and the description only implies its use without adding meaning. It does not explain what employee_id represents or how to obtain it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action 'Get leave history' and the resource 'the employee', but it does not distinguish from sibling tools like get_leave_balance or apply_leave. It is not a tautology, but specificity is limited.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as get_leave_balance. There is no mention of prerequisites, context, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

B3.2/5.0

Disambiguation5/5

Each tool clearly targets a distinct function: checking balance, applying for leave, and viewing history. No overlap or ambiguity.

Naming Consistency5/5

All tools follow a consistent 'verb_leave_noun' pattern (get_leave_balance, apply_leave, get_leave_history), making the set predictable.

Tool Count4/5

Three tools is minimal but reasonable for a basic leave manager. A few more (e.g., cancel or update leave) could be helpful, but the count is not inappropriate.

Completeness2/5

The set covers basic read and create operations but lacks essential operations like cancel, update, or approve leave, leaving significant gaps for a full leave management workflow.

Maintenance

ActivitySlowing

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

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MCP Server Setup Guide (Windows) — Leave Management Example

1. Project Setup

2. Example main.py

3. Test with MCP Inspector (recommended before touching Claude Desktop)

4. Connecting to Claude Desktop

4a. If Claude Desktop isn't installed yet

4b. Finding the right config file

4c. The config format

4d. Restart and verify

5. Quick troubleshooting checklist

6. Useful commands reference

Available Tools

Output Schema

Output Schema

Output Schema

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

2. Example `main.py`