Provides Fantasy Premier League advice including transfer recommendations, captain picks, and differential suggestions.
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., "@FPL Advisor MCP Server Recommend a captain from Haaland, Salah, Saka."
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 Model Context Protocol (MCP) server that gives Fantasy Premier League advice. Built with FastMCP.
Tool | Description |
| Suggest transfer targets based on form and value |
| Pick the best captain from your squad |
| Find low-ownership players under a price ceiling |
team_json is a JSON array of player names, e.g. '["Erling Haaland", "Mohamed Salah"]'.
Related MCP server: fpl-mcp
# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh
# Run with MCP Inspector (opens a browser UI to test tools)
uv run mcp dev server.py
# Or run the server directly
uv run python server.pyAdd this to your Claude Desktop config file:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"fpl-advisor": {
"command": "uv",
"args": ["run", "--directory", "/ABSOLUTE/PATH/TO/fpl-advisor-mcp", "python", "server.py"]
}
}
}Replace /ABSOLUTE/PATH/TO/fpl-advisor-mcp with the actual path, then restart Claude Desktop.
Add the MCP server to your project settings:
claude mcp add fpl-advisor -- uv run --directory /ABSOLUTE/PATH/TO/fpl-advisor-mcp python server.pyOnce connected, try these prompts in Claude Code:
Who should I captain this week? My team has Haaland, Salah, Saka, Palmer, and Watkins.I have £7m to spend — find me a differential pick that no one else owns.Suggest a transfer for my team: Haaland, Salah, Trent, Saka, Watkins.
Who gives me the best value for money?Compare Cole Palmer vs Bukayo Saka — who's the better FPL asset right now?Build me a front three under £30m using the best form-to-price ratio.Find low-ownership differential players under a given price.
Args: max_price: Maximum price (e.g. 7.0 for players £7.0m or under)
| Name | Required | Description | Default |
|---|---|---|---|
| max_price | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral transparency. It only states the search intent but does not disclose output format, sorting, or whether it is a read-only operation. It does not clarify what 'low-ownership' means or any side effects, leaving significant behavioral ambiguity.
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 concise, front-loaded with the main purpose, and includes a brief Args block. There is no redundant information, and the structure helps the agent quickly understand the tool and its parameter. Every sentence earns its place.
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?
The tool has a single parameter, which is well explained. An output schema exists (although not shown), so return-value details are not required in the description. The description provides sufficient context for the agent to select and invoke the tool, though it could benefit from a brief note on the nature of 'low-ownership'.
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 coverage is 0% (no descriptions in the schema). The description compensates fully by explaining the unit (£m) and inclusivity ('or under') with an example (7.0 for players £7.0m or under). This adds meaningful semantic value beyond the bare parameter name.
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 purpose: 'Find low-ownership differential players under a given price.' The verb 'find' combined with the specific resource ('low-ownership differential players') and constraint ('under a given price') clearly distinguishes it from sibling tools like recommend_transfer and recommend_captain.
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?
Usage is implied by the purpose (use this when looking for differentials under a price), but there is no explicit guidance on when to use this versus alternatives. The description does not mention when not to use it or compare it to the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pick the best captain from your FPL team.
Args: team_json: JSON array of player names currently in your team, e.g. '["Erling Haaland", "Mohamed Salah"]'
| Name | Required | Description | Default |
|---|---|---|---|
| team_json | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden. It does not disclose how the best captain is chosen, whether it uses external data, or any side effects. For a pure recommendation tool, this is a notable gap.
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 concise and front-loaded with the core purpose. The Args section is minimal and directly relevant, with no filler content.
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 simplicity and the existence of an output schema, the description covers the essential input semantics and purpose. However, it lacks any note on the algorithm or data used for 'best', which would enhance 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 schema only provides the type as string, but the description adds meaningful semantics: it specifies the input is a JSON array of player names and gives an example. This compensates for the 0% schema coverage, though it doesn't cover edge cases like invalid JSON or empty arrays.
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 action ('Pick the best captain') and the resource (the user's FPL team). It distinguishes itself from siblings like recommend_transfer and find_differentials by focusing on captain selection.
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 guidance is provided on when to use this tool versus recommend_transfer or find_differentials. The description only states what it does, not the context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Suggest transfer targets for your FPL team.
Args: team_json: JSON array of player names currently in your team, e.g. '["Erling Haaland", "Mohamed Salah"]'
| Name | Required | Description | Default |
|---|---|---|---|
| team_json | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states that it suggests transfer targets and describes the input format, but does not disclose side effects, permissions, rate limits, or any behavior beyond the core action.
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 extremely concise, with a clear one-sentence purpose followed by a parameter example. Every part earns its place, and there is no redundancy or filler.
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?
The tool has a simple interface with one parameter, and an output schema exists, so the description need not explain return values. However, it lacks contextual details such as assumptions about player names, error handling for invalid entries, or how suggestions are generated, leaving gaps given the lack of annotations.
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 provides only the parameter type (string) and title, with 0% description coverage. The description compensates effectively by explaining that team_json is a JSON array of player names and providing a concrete example, adding significant meaning beyond the schema.
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 it suggests transfer targets for an FPL team, using a specific verb and resource. This distinguishes it from the sibling tools (recommend_captain, find_differentials) by the action and goal, though no explicit comparison is made.
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?
There is no guidance on when to use this tool versus alternatives like recommend_captain or find_differentials. The context is only implied by the name and purpose, leaving the agent without explicit usage criteria.
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.
v0.1.0find_differentialsrecommend_captainrecommend_transferScored across 3 tools
Each tool serves a distinct purpose: recommending transfers, selecting a captain, and finding differentials. There is no overlap in functionality, and the descriptions make clear when to use each one.
All tool names follow a consistent verb_noun pattern (recommend_transfer, recommend_captain, find_differentials). While verbs differ, the structure is uniform and easily predictable.
The server has 3 tools, which is well within the ideal 3-15 range for a focused advisor. Each tool addresses a core decision area in FPL without unnecessary bloat.
The toolset covers key FPL decisions—transfers, captain, and differentials—but lacks additional advisory features like chip strategy or fixture difficulty analysis. These are minor gaps given the server's clear scope.
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