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4rgon4ut

Sutra

by 4rgon4ut
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Python
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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
understand_questionB

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_logicA

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.
backtrackingA

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_abstractA

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_architectureB
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_guideA
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_complexityC
Analyzes a task to recommend the most efficient tool (The Router).

Args:
    task_description: The user's prompt or task.
get_protocol_shellC
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_templateA

Returns the Python function for creating molecular contexts (Module 02). Use this to programmatically construct few-shot prompts.

get_prompt_programB
Returns a functional pseudo-code prompt template (Module 07).

Args:
    program_type: The type of program ('math', 'debate').
get_cell_protocolA
Returns a cell protocol template describing memory behaviors.

Args:
    name: Identifier of the cell protocol (key_value, windowed, episodic).
get_organB
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

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
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).
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.5/5.0

Scored across 12 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: analyzing complexity, backtracking, architecture design, retrieving various templates (cell protocol, molecular, organ, prompt program, protocol shell, technique guide), symbolic abstraction, question decomposition, and logic verification. No overlapping functionality.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., analyze_task_complexity, get_cell_protocol). However, 'backtracking' is a gerund and 'symbolic_abstract' combines an adjective with a verb, deviating slightly from the prevailing pattern.

Tool Count5/5

With 12 tools, the server is well-scoped for its domain of context engineering. Each tool earns its place, covering analysis, design, template retrieval, and verification without being overwhelming or sparse.

Completeness4/5

The tool set covers core workflows: analyzing tasks, designing architecture, retrieving building blocks, and verifying logic. A minor gap is the lack of tools for executing or instantiating the designed blueprints, but the provided surface is largely complete for planning and template access.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources