AI Worlds CLI Knowledge

Consult the Strongest Available Model for One Hard Problem

worlds_advise_hard_problem

Read-onlyIdempotent

Choose the maximum-depth model-and-effort pairing for one difficult problem within the caller's actual CLI set. For a single CLI it selects that provider's strongest catalog role; with multiple CLIs it returns provider candidates but does not invent a universal cross-provider winner. It uses single-task depth, never ultra or ultracode orchestration, defaults to read-only, and does not launch a CLI or grant permissions.

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": {
      "task": "<task>",
      "current_cli": "<current_cli>"
    },
    "name": "worlds_advise_hard_problem"
  }
}

Input Schema

Name	Required	Description	Default
`task`	Yes
`current_cli`	Yes
`preferred_cli`	No	Optional user-selected provider. This is an explicit preference, not a benchmark verdict.
`available_clis`	No
`selection_scope`	No		current-cli
`execution_intent`	No		read-only

Output Schema

Name	Required	Description	Default
`task`	Yes
`safety`	Yes
`evidence`	Yes
`current_cli`	Yes
`decision_rule`	Yes
`recommendation`	Yes
`advisor_version`	Yes
`selection_scope`	Yes
`provider_candidates`	Yes
`live_validation_steps`	Yes
`cross_provider_verdict`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 2, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses key behavioral traits: 'uses single-task depth, never ultra or ultracode orchestration, defaults to read-only, and does not launch a CLI or grant permissions.' It also explains the cross-provider candidate behavior, which is valuable context not captured in structured metadata.

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 a tight paragraph of four sentences, front-loaded with the main purpose. Each sentence adds useful context without being verbose. It could be slightly more compact, but overall it is well-structured and efficient.

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?

Given the tool's advisory nature and the existence of an output schema, the description covers the core selection logic and key constraints (read-only, no launch, no orchestration). It lacks detail on how selection_scope and execution_intent affect results, but these are partially self-explanatory from enums. The description is complete enough for an agent to invoke correctly.

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

Parameters2/5

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

The description does not explain any of the parameter semantics. Schema coverage is only 17%, and the description only indirectly references CLI context ('within the caller's actual CLI set', 'single CLI', 'multiple CLIs') but never defines task, current_cli, available_clis, selection_scope, or execution_intent. The description fails to compensate for the low 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 uses specific verbs and resources: 'Choose the maximum-depth model-and-effort pairing for one difficult problem within the caller's actual CLI set.' This clearly distinguishes the tool from siblings like worlds_compare_cli_workflows or worlds_plan_cli_work by focusing on selecting the strongest model for a single hard problem.

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 gives clear context on when to use the tool (for one difficult problem) and exclusions: 'never ultra or ultracode orchestration,' 'does not launch a CLI or grant permissions.' It also explains behavior for single vs. multiple CLIs, but doesn't explicitly name alternative sibling tools or give a formal '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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