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hkaanengin

opendota-mcp-server

by hkaanengin

get_match_details

Fetch detailed Dota 2 match data, covering teamfights, objectives, gold advantage, and player benchmarks, with automatic parse status detection.

Instructions

Get comprehensive details for a specific match, with automatic detection of parse status.

Use this when users ask:

  • "Show me match [match_id]"

  • "Analyze match [match_id]"

  • "What happened in match [match_id]?"

  • "Show me the teamfights in match [match_id]"

  • "Who won match [match_id]?"

  • "What were the item builds in match [match_id]?"

  • "Show me the gold graph for match [match_id]"

This tool automatically detects whether a match has been parsed (has detailed replay data) or not:

  • Parsed matches: Returns organized summary with teamfights, objectives, gold/XP advantage graphs, chat logs, and detailed player performance benchmarks

  • Unparsed matches: Returns basic match data with player KDA, GPM/XPM, and game outcome

For matches that aren't parsed yet, use request_parse_match() first, wait a few minutes, then call this function to get the detailed data.

Args: match_id: The match ID to retrieve (e.g., 8123456789)

Returns: Dictionary with structure depending on parse status:

If parsed=True (detailed replay data available):
- parsed (bool): True
- metadata (dict): Match info (duration, game mode, winner, etc.)
- teamfights_summary (dict):
    - count (int): Number of teamfights detected
    - teamfights (list): Detailed teamfight breakdowns with:
        - start/end time, location, deaths, gold swing
- objectives (list): Timeline of objectives (towers, barracks, Roshan)
- chat (list): In-game chat messages with timestamps
- picks_bans (list): Draft phase picks and bans
- players_summary (dict):
    - count (int): Number of players (always 10)
    - players (list): Per-player statistics including:
        - account_id, hero_name, team, KDA
        - gold_per_min, xp_per_min, net_worth
        - hero_damage, tower_damage, hero_healing
        - last_hits, denies
        - benchmarks: Performance percentiles for 7 key metrics
- gold_advantage (list): Gold advantage over time (array of values)
- xp_advantage (list): XP advantage over time (array of values)

If parsed=False (basic match data only):
- parsed (bool): False
- data (dict):
    - players (list): Basic player stats (KDA, GPM, damage, benchmarks)
    - radiant_win (bool): True if Radiant won, False if Dire won
    - duration (str): Game length in MM:SS format
    - match_id (int): The match ID

Common queries: - Basic match info: get_match_details(8123456789) - Teamfight analysis: After parsing, check teamfights_summary - Performance comparison: Compare benchmarks between players - Gold advantage: Check gold_advantage array for momentum swings

Example (parsed match): get_match_details(8123456789) -> { "parsed": true, "metadata": { "match_id": 8123456789, "duration": "45:23", "radiant_win": true, "game_mode": 22 }, "teamfights_summary": { "count": 12, "teamfights": [ { "start": 1245, "end": 1267, "deaths": 6, "gold_delta": 3500, ... } ] }, "players_summary": { "count": 10, "players": [ { "hero_name": "Rubick", "team": "radiant", "kills": 8, "deaths": 5, "assists": 25, "gold_per_min": 380, "benchmarks": { "gold_per_min": {"raw": 380, "pct": 65.3}, "xp_per_min": {"raw": 425, "pct": 58.2}, ... } }, ... ] }, "gold_advantage": [0, 200, 450, 800, ...], ... }

Example (unparsed match): get_match_details(8123456789) -> { "parsed": false, "data": { "players": [...], "radiant_win": true, "duration": "45:23", "match_id": 8123456789 } }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
match_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries full burden and excels. It discloses the automatic parse-status detection, explains the two distinct return structures (parsed/unparsed), and notes the prerequisite for unparsed matches. The behavior is fully transparent and includes practical details like 'wait a few minutes.'

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 well-structured with clear headers, bullet lists, and examples, and the purpose is front-loaded. However, it is notably long, and the extensive return-structure documentation may be redundant given an output schema exists. Still, the organization keeps it scannable.

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?

The description addresses the core complexity: conditional behavior based on parse status, the prerequisite parse workflow, and detailed semantics of both response shapes. It also includes common queries and examples, leaving minimal ambiguity for an agent to select and invoke the tool correctly.

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 0% for match_id, so the description must compensate. It does so by defining match_id as 'The match ID to retrieve' with a concrete example (8123456789). For a single integer parameter, this is sufficient, though it doesn't add constraints (e.g., format/range).

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 begins with a clear, specific verb+resource combination: 'Get comprehensive details for a specific match.' It further distinguishes itself from siblings like request_parse_match and get_hero_details by focusing on match-level data and directly listing user query examples that trigger its use.

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

Usage Guidelines5/5

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

The description provides explicit when-to-use guidance through example user queries, and clearly differentiates from the alternative request_parse_match: 'For matches that aren't parsed yet, use request_parse_match() first, wait a few minutes, then call this function.' This is a concrete workflow with an alternative named.

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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