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Crawlora MCP

chesscom_player_is_online

Read-only

Get the source online-status value for one Chess.com username. The service calls Chess.com’s documented Published-Data API serially. This independent service is not affiliated with Chess.com; do not use results for AI training or dataset creation.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "username": "<username>"
    },
    "name": "chesscom_player_is_online"
  }
}

Input Schema

NameRequiredDescriptionDefault
usernameYesRequired source username.

Output Schema

NameRequiredDescriptionDefault
dataYesThe tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered; the description adds real context by disclosing that requests hit Chess.com's Published-Data API serially and that the service is unaffiliated. It does not say what happens for unknown usernames or how failures surface, leaving a small gap.

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 operative purpose is front-loaded in the first sentence, with the API and affiliation caveats following. The final sentence is compliance boilerplate rather than tool-selection information, but it is brief and relevant to permissible use.

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?

An output schema exists, so return values need not be described, and the single required parameter is fully documented in the schema. Combined with the annotation coverage, only the error/missing-user behavior is left unspecified, which is a minor omission for a simple one-parameter read.

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?

With a single parameter at 100% schema description coverage, the schema already defines the username field, and the description only reinforces that it is one source username. The baseline of 3 applies because no additional syntax, format, or validation detail is contributed.

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 names a specific verb and resource ("Get the source online-status value for one Chess.com username"), which is unambiguous and distinguishable from sibling reads like profile or stats. It stops short of explicitly contrasting itself with those siblings, but the scope is clear from the text alone.

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?

There is no statement of when to call this versus siblings such as chesscom_player_profile or chesscom_player_live_games, and no prerequisites or exclusions about the target account. The only directive is a downstream restriction ("do not use results for AI training or dataset creation"), which governs use of the output rather than tool selection.

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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