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tournament_upcoming_official

Fetch official upcoming Splinterlands tournaments for a provided username using a single GET request.

Instructions

Read the official upcoming tournament route. It returned exactly the same 58 rows as upcoming in the capture; do not infer permanent equivalence or a distinct complete dataset. Makes one logical GET request Does not auto-fetch continuation pages. Required inputs reflect tool policy as well as measured upstream requirements. Other declared filters are forwarded as supplied; their effectiveness is not implied by the schema. Array responses are locally limited to 100 rows and 256 KiB, with truncation reported in text and metadata. Oversized records are refused without partial fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
usernameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.0

TDQS

A3.7/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly: it states it makes one logical GET request, does not auto-fetch continuation pages, locally limits arrays to 100 rows and 256 KiB, reports truncation, refuses oversized records, and notes that filter effectiveness is not guaranteed. This is exemplary disclosure.

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 front-loaded with the core purpose and then lists relevant behavioral caveats in a logical order. Most sentences earn their place, though the 'Required inputs reflect tool policy' sentence is cryptic and adds limited actionable value.

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

Completeness3/5

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

For a simple one-parameter read tool, the description covers dataset identity, pagination, limits, and truncation behavior well. However, with no output schema, it does not describe the shape or fields of the returned tournament rows, and it omits username semantics, leaving an agent partially under-informed.

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

Parameters1/5

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

The schema has one optional username with 0% description coverage, and the description never explains what username means or how it affects the response. The vague statements about 'required inputs' and 'other declared filters' are confusing because the schema declares no required inputs and only one property, so they add no real parameter meaning.

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 opens with a specific verb and resource: 'Read the official upcoming tournament route.' It also distinguishes itself from the sibling 'upcoming' by noting the identical 58-row capture and warning against inferring permanent equivalence, which clarifies its scope.

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

Usage Guidelines3/5

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

Usage context is implied through the name 'official' and the comparison to 'upcoming', but the description never explicitly says when to choose this tool over tournament_upcoming or other tournament siblings. The 'do not infer permanent equivalence' caveat provides interpretation guidance but no direct when-to-use/when-not-to-use statement.

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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