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Constellation Composition MCP Server

by dmarsters

Apply Rhythmic Preset — Generate Oscillation Sequence

apply_constellation_preset
Read-onlyIdempotent

Generate a complete oscillation sequence for AI image generation by applying a rhythmic preset that produces 5D coordinate states for orbit integration and keyframe prompts.

Instructions

Apply a Phase 2.6 rhythmic preset, generating a complete oscillation sequence over one full period.

Layer 2: Deterministic sequence generation (0 tokens).

Returns a trajectory of 5D coordinate states tracing one complete cycle of the preset oscillation. This output is directly usable by:

  • Tier 4D integrate_forced_limit_cycle for single-domain orbits

  • Tier 4D integrate_forced_limit_cycle_multi_domain for composition

  • Phase 2.7 generate_constellation_attractor_prompt for keyframe prompts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYesInput for applying a rhythmic preset.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The implementation of the 'apply_constellation_preset' tool, which takes a rhythmic preset name as input and generates a complete oscillation trajectory of 5D coordinates over one full cycle.
        name="apply_constellation_preset",
        annotations={
            "title": "Apply Rhythmic Preset — Generate Oscillation Sequence",
            "readOnlyHint": True,
            "destructiveHint": False,
            "idempotentHint": True,
            "openWorldHint": False
        }
    )
    async def apply_constellation_preset(params: RhythmicPresetInput) -> str:
        """
        Apply a Phase 2.6 rhythmic preset, generating a complete oscillation
        sequence over one full period.
    
        Layer 2: Deterministic sequence generation (0 tokens).
    
        Returns a trajectory of 5D coordinate states tracing one complete cycle
        of the preset oscillation. This output is directly usable by:
        - Tier 4D integrate_forced_limit_cycle for single-domain orbits
        - Tier 4D integrate_forced_limit_cycle_multi_domain for composition
        - Phase 2.7 generate_constellation_attractor_prompt for keyframe prompts
        """
        cfg = CONSTELLATION_RHYTHMIC_PRESETS[params.preset_name]
        trajectory = _generate_preset_trajectory(cfg)
    
        return json.dumps({
            "preset_name": params.preset_name,
            "period": cfg["steps_per_cycle"],
            "pattern": cfg["pattern"],
            "state_a": cfg["state_a"],
            "state_b": cfg["state_b"],
            "description": cfg["description"],
            "parameter_names": CONSTELLATION_PARAMETER_NAMES,
            "trajectory": trajectory,
            "trajectory_length": len(trajectory)
        }, indent=2)
Behavior4/5

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

The description adds valuable behavioral context beyond annotations by specifying 'Layer 2: Deterministic sequence generation (0 tokens)' and describing the output format ('trajectory of 5D coordinate states'). While annotations cover safety (readOnlyHint=true, destructiveHint=false), the description provides implementation details about the generation process and output structure that aren't captured in annotations.

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 well-structured and efficiently communicates purpose, process, and usage in three focused sentences. Each sentence serves a distinct purpose without redundancy, though the technical terminology makes it somewhat dense.

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

Completeness5/5

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

Given the presence of comprehensive annotations (readOnlyHint, idempotentHint, etc.), 100% schema coverage, and an output schema (implied by 'Has output schema: true'), the description provides excellent contextual completeness. It explains what the tool does, how it fits into workflows, and what the output contains without needing to repeat structured information.

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 100% schema description coverage, the input schema already fully documents the single parameter. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation without providing additional semantic context about parameters.

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 clearly states the specific action ('Apply a Phase 2.6 rhythmic preset') and the outcome ('generating a complete oscillation sequence over one full period'). It distinguishes itself from siblings by focusing on preset application rather than computation, generation, or listing operations mentioned in sibling tools.

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?

The description explicitly states when to use this tool's output by naming three specific downstream tools ('Tier 4D integrate_forced_limit_cycle', 'Tier 4D integrate_forced_limit_cycle_multi_domain', 'Phase 2.7 generate_constellation_attractor_prompt'). This provides clear guidance on the tool's role in a workflow context.

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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