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Crawlora MCP

by net.crawlora
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vlr_search

Read-only

Searches VLR.gg for teams, events and players by name and returns all matches on one page: kind, id, name, URL, logo, event date range and prize pool, and for retired teams the inactive flag and successor team. Provide q (the name to find) and optionally type (all, teams, events or players). Use it to find the id that vlr_team, vlr_event, vlr_player and the history tools require.

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": {
      "q": "<q>"
    },
    "name": "vlr_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
qYesRequired search text, 1 to 100 characters, for example sentinels or tenz.
typeNoOptional result group: all (default), teams, events or players.

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

A4.3/5.0
Behavior4/5

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

Annotations already cover the safe/open-world profile, but the description adds non-obvious behavior: results come back 'on one page' with no pagination, and retired teams carry an inactive flag plus successor team. It stops short of describing result ordering, match limits, or behavior on zero hits.

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?

Front-loaded with the action and scope, then the return fields, then the routing sentence. It is dense but every clause carries information; only the return-field enumeration is arguably redundant given the output schema, which keeps it from a 5.

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 two-parameter lookup with an output schema and read-only annotations, this covers purpose, parameters, result shape and downstream usage. The remaining gap is matching semantics (does it find partial names? does it rank results?), which matters for an agent choosing what to put in q.

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 both parameters are already fully documented including the 1-100 character constraint and the enum-like type values. The description restates q and the four type options without adding format or matching-behavior detail (e.g. partial vs exact name matching).

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?

States a specific verb and resource ('Searches VLR.gg for teams, events and players by name') and names the three entity kinds searched. An agent can distinguish it from vlr_team, vlr_event and vlr_player without opening any schema, since those are lookups by id while this is the name-based resolver.

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?

Explicitly routes the agent: 'Use it to find the id that vlr_team, vlr_event, vlr_player and the history tools require.' That names the alternatives and the condition that selects this tool. It also tells the agent to supply q and optionally type, so there is nothing left to infer.

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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