Skip to main content
Glama

get_player_counts

Get a player's match counts broken down by leaver status, game mode, lobby type, lane role, region, and patch, with names resolved.

Instructions

Match counts broken down by leaver status, game mode, lobby type, lane role, region and patch, with ids resolved to readable names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
winNo1 = wins only, 0 = losses only.
dateNoOnly matches from the last N days.
sortNoSort results by this field, descending (e.g. 'start_time').
limitNoMax results to return.
patchNoPatch id filter (e.g. from get_constants patch).
havingNoMin games played (used by hero-stat tools).
offsetNoSkip this many results (pagination).
regionNoRegion id filter.
hero_idNoOnly matches on this hero id.
game_modeNoGame mode id filter (e.g. 22 = Ranked All Pick).
lane_roleNoLane role: 1=Safe, 2=Mid, 3=Off, 4=Jungle.
account_idYesSteam32 account id (the number after /players/ in an OpenDota profile URL, or from search_players).
is_radiantNo1 = Radiant side only, 0 = Dire only.
lobby_typeNoLobby type id filter (e.g. 7 = Ranked).
significantNo0 (this server's default) includes ALL game modes including Turbo; 1 = only standard/ranked-style modes (OpenDota's own default, which hides Turbo).
with_hero_idNoOnly matches with these heroes on the player's team.
against_hero_idNoOnly matches against these heroes.
excluded_account_idNoExclude matches where these account ids played.
included_account_idNoOnly matches where these account ids played.
Install Server

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. The description states that ids are resolved to readable names, which is a helpful behavioral note, but it doesn't mention potential performance issues with no date/limit filters, default sort order, or exactly how the 'having' field behaves (it says 'used by hero-stat tools' but not this tool).

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?

One sentence, front-loaded with the core purpose ('Match counts broken down by...'), and ends with the id-resolution behavior. Every word earns its place; no repetition of schema details.

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

Completeness4/5

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

For a read-only aggregation tool with 100% schema coverage and no output schema, the description is reasonably complete. It could mention that 'having' may not apply or clarify defaults, but the schema already covers parameter semantics, and the description conveys the grouping behavior that defines this tool's distinct value.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 19 parameters. The description adds little beyond the schema—it lists breakdown dimensions but doesn't explain how they map to query parameters or clarify the 'having' parameter's role in this tool. Baseline 3 is appropriate given the complete schema coverage.

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 that this tool returns 'match counts' broken down by multiple dimensions (leaver status, game mode, lobby type, lane role, region, patch) and resolves ids to readable names. It uses a specific verb ('get') and resource ('player counts'), and the breakdown dimensions distinguish it from sibling tools like get_player_matches or get_player_win_loss.

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?

The description implies a read-only aggregation use case: counting matches with filters and grouping dimensions. It does not explicitly name alternative tools or when-not-to-use conditions, but the breakdown-by-categories wording makes its use case reasonably clear relative to siblings such as get_player_totals or get_player_matches.

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

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yuxiang115/opendota-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server