sol-luna-orchestrator

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Explore repository, API, or documentation with Luna

explore

Investigate an admitted repository, API, or documentation scope to return provenance-marked facts, inferences, and unknowns from a read-only disposable run. Get verified insight before implementation.

Instructions

Explicitly explore an admitted repository, API, or documentation scope with gpt-5.6-luna; fixed read-only disposable execution returns provenance-marked worker claims, runtime facts, inferences, and unknowns. Implements nothing, cannot delegate, and is never automatic.

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": {
      "target": "<target>",
      "effortReason": "<effortReason>",
      "scope": "<scope>"
    },
    "name": "explore"
  }
}

Input Schema

Name	Required	Description	Default
`scope`	Yes
`effort`	No		high
`target`	Yes
`context`	No
`questions`	No
`effortReason`	Yes
`resultDetail`	No		handoff
`activityLabel`	No
`computePolicy`	No	Optional per-call compute envelope. Narrows this installation's operator-owned baseline only and can never widen it. Omit to use the baseline.
`contextCapsule`	No
`forbiddenFiles`	No
`timeoutSeconds`	No
`workingDirectory`	No

Schema Changelog

Changes observed during successful MCP inspections.

Addedv0.11.0Aug 29, 2026

TDQS

A4.1/5.0

Behavior5/5

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

With no annotations, the description carries the full burden and does so unusually well. It discloses that execution is 'fixed read-only disposable,' that it returns 'provenance-marked worker claims, runtime facts, inferences, and unknowns,' and that it 'implements nothing, cannot delegate, and is never automatic.' This gives an agent a clear safety and side-effect profile.

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?

The description is two efficient sentences with zero filler. Purpose and scope are front-loaded, followed immediately by behavioral disclosures and exclusions. Every clause adds information.

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

Completeness2/5

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

This is a complex tool — 13 parameters, nested objects, no output schema, and no annotations — so the description alone is insufficient for correct invocation. It explains the high-level behavior well but does not clarify target vs scope, effortReason requirements, resultDetail choices, computePolicy, or output structure. An agent could select the tool but would struggle to call it 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?

Schema description coverage is only 8%, and the description barely compensates: 'repository, API, or documentation scope' hints at target/scope, but the 13 parameters — including required effortReason — receive no meaningful explanation. The schema itself has no descriptions for most properties, so an agent lacks essential parameter semantics.

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 a specific verb and object phrase: 'Explicitly explore an admitted repository, API, or documentation scope' — so an agent knows exactly what the tool acts on. It also differentiates from siblings by adding 'Implements nothing, cannot delegate, and is never automatic,' which separates it from delegate_task and routing_preflight. This goes beyond a generic restatement.

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 opening clause gives clear usage context: use this for explicit exploration of an admitted scope. The additonal exclusions — 'cannot delegate' and 'never automatic' — tell an agent when not to expect delegation or autonimous routing. Alternatives are not explicitly named, but the behavior boundaries are strong enough to guide 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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