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fpl-context-mcp

by sbanthia92
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

query_historical_stats

Run read-only SQL queries against the FPL stats database to answer questions about player stats, fixtures, team strength, and gameweek history across seasons.

Instructions

Execute a read-only SQL SELECT against the FPL stats database. Use this to answer questions about player stats, fixtures, team strength, or gameweek history for the seasons in the database.

Available tables (read-only). The current season is fully populated; past seasons exist only as season totals in players (see notes below the table list):

seasons — id, label (e.g. '2025/26'), start_year, is_current teams — season_id, fpl_id, name, short_name, strength, strength_attack_home/away, strength_defence_home/away gameweeks — season_id, gw_number (1–38), deadline_time, is_current, is_next, is_finished, average_entry_score, highest_score players — season_id, fpl_id, team_fpl_id, first_name, second_name, web_name, position (GKP/DEF/MID/FWD), now_cost, form, total_points, minutes, goals_scored, assists, clean_sheets, expected_goals, expected_assists, ict_index, status, news fixtures — season_id, fpl_id, gw_number, kickoff_time, home_team_fpl_id, away_team_fpl_id, home_score, away_score, finished, home_team_difficulty, away_team_difficulty gw_player_stats — season_id, player_fpl_id, gw_number, fixture_fpl_id, opponent_team_fpl_id, was_home, minutes, goals_scored, assists, clean_sheets, bonus, total_points, expected_goals, expected_assists, ict_index, starts

Notes:

  • teams, fixtures, gameweeks and gw_player_stats hold the CURRENT season only.

  • For past seasons, players has one row per player per season with season totals (points, minutes, goals, assists, clean sheets, cards, bonus). team_fpl_id is NULL there, and fpl_id is the player's current FPL id. Join seasons for the label.

  • Past seasons only include players in the CURRENT FPL player list. Departed players are absent, so league-wide or team-wide totals for past seasons are incomplete; only trust per-player history for players who appear in the current season.

Join hint: teams.fpl_id = players.team_fpl_id (current season, same season_id).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlYesA SQL SELECT statement. Only SELECT is allowed — mutations will be rejected. LIMIT is injected automatically if omitted (max 100 rows).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries full disclosure burden. It states the operation is read-only, enumerates the exposed tables, and discloses critical behavioral caveats: current-season-only tables, past-season rows lacking team_fpl_id, and incomplete past-season league/team totals due to departed players being absent. This is exactly the kind of context an agent needs before generating SQL.

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?

The description is long, but the length is earned: it provides a full denormalized schema for a SQL tool. It is front-loaded with the purpose, then structured into table list, notes, and join hint. Minor redundancy ('read-only' appears twice) keeps it from a 5.

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

Completeness5/5

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

For a read-only SQL tool with no output schema, this is complete: it covers what can be queried, table relationships, current vs. past season behavior, and data-quality limitations. No required input or result-shape information is missing.

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

Parameters4/5

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

Schema coverage is 100% for the single sql parameter, so the baseline is 3. The description adds substantial meaning beyond the schema by providing the full table/column inventory, join hints, and season-scoping notes, enabling the agent to write realistic SELECT statements rather than guessing table names.

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?

Description opens with a specific verb and resource: 'Execute a read-only SQL SELECT against the FPL stats database.' It then names the exact question types in scope (player stats, fixtures, team strength, gameweek history), and the table list makes clear it is a general stats/history query tool, distinguishing it from the press-conference sibling.

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

Usage Guidelines4/5

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

'Use this to answer questions about player stats, fixtures, team strength, or gameweek history' gives direct guidance on when to call it. It does not explicitly name the sibling alternative or state exclusions (e.g., press-conference questions), but with only one sibling and a clear scope, the context is strong.

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

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