Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| understand_question | Produce a protocol shell to decompose a user question. Args:
question: The raw user ask to unpack.
context: Optional background knowledge or situational frame.
constraints: Explicit limits or success criteria.
Returns:
A structured prompt guiding the model to restate intent, surface
constraints, and prepare clarifying questions before acting.
|
| verify_logic | Generate a verification protocol for a reasoning trace. Args:
claim: The headline answer or assertion to validate.
reasoning_trace: The supporting chain-of-thought or proof steps.
constraints: Optional guardrails (requirements, risk limits).
Returns:
Structured prompt that audits assumptions, inference steps, and
evidence, then proposes patches for any defects.
|
| backtracking | Produce a recursive backtracking scaffold for error correction. Args:
objective: Overall goal to satisfy.
failed_step: The step or subgoal that failed.
trace: Optional reasoning trace leading to the failure.
constraints: Guardrails or requirements to respect.
Returns:
Structured prompt that rewinds to last stable state, explores
alternatives, and proposes a patched plan.
|
| symbolic_abstract | Convert a concrete expression into abstract variables for reasoning. Args:
expression: The raw text or equation to abstract.
mapping_hint: Optional guidance for token-to-symbol mapping.
goal: Optional downstream task (e.g., simplify, prove, generalize).
Returns:
Structured prompt that maps tokens to symbols, restates the problem
abstractly, and provides a reversible mapping table.
|
| design_context_architecture | Architects a custom context system based on a high-level goal (The Architect).
Returns a blueprint of Sutra components (Molecules, Cells, Organs, Thinking Models).
Use this when the user wants to build a persistent agent or complex workflow
rather than solving a single immediate task.
Args:
goal: The user's objective (e.g., "Build a writing assistant that learns my style").
constraints: Optional limits (e.g., "Must be lightweight"). |
| get_technique_guide | Returns a guide to available Context Engineering techniques (The Librarian).
Use this to discover the best tool for a given task.
Args:
category: Filter by 'reasoning', 'workflow', 'code', 'project', or 'all'. |
| analyze_task_complexity | Analyzes a task to recommend the most efficient tool (The Router).
Args:
task_description: The user's prompt or task. |
| get_protocol_shell | Returns a Protocol Shell. Can return a specific pre-defined template or a blank shell.
Args:
name: The name of the protocol (e.g., 'reasoning.systematic') OR a custom name.
intent: (Optional) The intent if creating a custom shell. |
| get_molecular_template | Returns the Python function for creating molecular contexts (Module 02). Use this to programmatically construct few-shot prompts. |
| get_prompt_program | Returns a functional pseudo-code prompt template (Module 07).
Args:
program_type: The type of program ('math', 'debate'). |
| get_cell_protocol | Returns a cell protocol template describing memory behaviors.
Args:
name: Identifier of the cell protocol (key_value, windowed, episodic). |
| get_organ | Returns an organ template for multi-agent orchestration (Layer 4).
Organs combine programs and cells into cohesive workflows for complex tasks
requiring multi-perspective analysis or collaborative reasoning.
Args:
name: Identifier of the organ ('debate_council' for multi-perspective debate). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| get_cot_molecules | Returns Chain-of-Thought templates (Module 02). |
| get_reference_layers | Returns the Context Engineering Layer definitions. |
| get_neural_fields | Returns Neural Field primitives (Module 08-10). |