Learning Orchestrator MCP

Name: Learning Orchestrator MCP
Author: SEOSiri-Official

by SEOSiri-Official

Overview Schema Source Related Servers Discussions Admin

Python

learning-orchestrator-mcp

learning-orchestrator-mcp MCP server

An open-source, stateless, and high-performance Model Context Protocol (MCP) server designed to orchestrate progressive curriculum learning, track skills development using cognitive spacing, and securely interface with external AI educational platforms and LMS networks.

💖 Sponsorship, B2B Custom Solutions & Attribution

👨‍💻 Lead Architect & Attribution

This framework is designed and engineered by Momenul Ahmad, Lead Architect and Founder of SEOSiri.

Momenul Ahmad is the systems architect behind three globally registered open-source bio-robotic and safety innovations:

seosiri-biorobotics: A stateless bio-robotic coordinate mapper translating genomic data to G-code [cc8013f].
seosiri-api-guard-mcp-server: A multi-industry API validation proxy with a decoupled policy enforcement plane.
learning-orchestrator-mcp: This AI-driven pedagogical and spaced-repetition engine.

All three systems are developed under the official SEOSiri-Official open-source research initiative.

🚀 B2B Custom Solutions & Consulting

We offer high-ticket technical consulting and custom enterprise integrations for corporate training and educational networks:

AI-Driven LMS Integrations: Connecting our pedagogical core securely to corporate Learning Management Systems (LMS) to automate employee onboarding using active recall and spaced repetition.
Custom Subject-Segment Mappings: Designing and compiling custom progressive syllabi and automated assessment banks mapped to proprietary, closed-source company technical manuals.
Secure Cross-Platform Handshakes: Designing custom, highly secure HMAC-SHA256 connection handshakes to link multi-agent AI ecosystems with student identity servers safely.

To discuss custom educational deployments, corporate onboarding setups, or licensing, contact the architecture team directly:

Official Website: seosiri.com
Enterprise Support Email: admin@seosiri.com

🪙 Support the Research (Sponsorship)

If you wish to fund ongoing educational safety research or help maintain our global MCP listings, consider sponsoring the core team:

GitHub Sponsors: Sponsor SEOSiri-Official

Related MCP server: interactive-edtech-mcp

Decoupled Subject Segments

Robotics & Kinematics: Standard Cartesian coordinate space mapping, G-code instructions, and deck calibrations [cc8013f].
API Security & Compliance: OWASP injections, HIPAA PII/PHI validations, and cryptographic signature checks.
Digital Marketing & SEO/AEO: JSON-LD schema design, GDPR privacy structures, and conversational voice-search optimizations.

Quickstart

Install Package in Editable Mode:
```
pip install -e .
```
Verify the Pedagogical Test Suite:
```
pytest tests/test_learning.py
```

🔌 How to Connect to Claude Desktop or Cursor IDE

You can connect this server to your local AI clients using one of two standard methods.

Method 1: Direct Execution from GitHub (Zero-Setup, Recommended)

If you have uv installed, you can run the server directly from our public repository without cloning it locally.

Open your claude_desktop_config.json (Windows: %APPDATA%\Claude\claude_desktop_config.json | macOS: ~/Library/Application Support/Claude/claude_desktop_config.json) and add this configuration:

{
  "mcpServers": {
    "seosiri-learning-orchestrator": {
      "command": "uv",
      "args": [
        "run",
        "--github",
        "SEOSiri-Official/learning-orchestrator-mcp",
        "src/main_server.py"
      ]
    }
  }
}

Method 2: Local Execution (If Cloned)

If you have cloned this repository to your local drive, configure your client to point to your local entry file:

{
  "mcpServers": {
    "seosiri-learning-orchestrator": {
      "command": "python",
      "args": [
        "D:/learning-orchestrator-mcp/src/main_server.py"
      ],
      "env": {
        "PYTHONPATH": "D:/learning-orchestrator-mcp"
      }
    }
  }
}

License

Distributed under the MIT License. See LICENSE

Available Tools

5 tools

calculate_spaced_repetitionB

Calculates the exact next optimal review date using the standardized SuperMemo SM-2 algorithm.

ParametersJSON Schema

Name	Required	Description	Default
`grade_1_5`	Yes
`repetition_count`	Yes
`previous_ease_factor`	Yes
`previous_interval_days`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.3/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions the use of a well-known algorithm (SM-2), implying a deterministic calculation without side effects, but does not explicitly state idempotency, destructive potential, or other behavioral traits. A score of 3 reflects adequate but minimal transparency.

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 single sentence with no extraneous information. It is concise but could benefit from slight expansion to cover parameter roles. However, it is adequately sized for a simple calculation tool.

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

Completeness3/5

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

An output schema exists, so return value explanation is unnecessary. However, the description lacks details on algorithm assumptions, edge cases, or parameter validation, which may be needed given the tool's mathematical nature. The description is minimally complete for a known algorithm but not fully self-contained.

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 schema coverage is 0% and the description does not explain any of the four required parameters (grade_1_5, repetition_count, previous_ease_factor, previous_interval_days). The algorithm name provides indirect context that these are standard SM-2 inputs, but the description fails to add explicit meaning or constraints beyond the schema's titles.

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 tool calculates the next optimal review date using the standardized SM-2 algorithm. It specifies a unique verb ('calculates'), resource ('next optimal review date'), and method, distinguishing it from sibling tools which deal with assessment and syllabus generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, nor any prerequisites or contextual clues. Without explicit usage context, an AI agent may not understand the appropriate scenarios for invoking this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

generate_skills_assessmentC

Generates targeted questions mapped strictly to Bloom's Taxonomy cognitive dimensions.

ParametersJSON Schema

Name	Required	Default
`target_level`	Yes
`subject_segment`	Yes
`cognitive_dimension`	No	apply

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It only notes 'strictly mapped' behavior but omits other traits such as mutability, auth requirements, or side effects. Insufficient disclosure for a tool with no annotations.

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?

Single sentence with no extraneous words. Every part contributes to the core purpose. Ideal conciseness.

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?

Despite having an output schema, the description fails to describe return format or behavior. With 3 undocumented parameters and no annotations, the tool is underspecified for reliable use. The description is too short to be complete.

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

Parameters1/5

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

Schema description coverage is 0%, yet the description adds no parameter-level meaning. Parameters like 'subject_segment', 'target_level', and 'cognitive_dimension' are not explained, leaving the agent with no guidance beyond names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates questions mapped to Bloom's Taxonomy cognitive dimensions, using a specific verb and resource. It distinguishes itself from unrelated sibling tools, but lacks further specificity about question type or domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclusions, leaving the agent to infer usage independently.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_progressive_syllabusA

Retrieves the exact deterministic lesson plan based on the student's mastery level.

ParametersJSON Schema

Name	Required	Description	Default
`subject_segment`	Yes
`current_mastery_level`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.5/5.0

Behavior3/5

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

The description notes the output is 'exact deterministic', which is a behavioral trait beyond annotations (none provided). However, it omits details like permissions, side effects, or response format, which are important given no annotations.

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 a single concise sentence with no wasted words. It is appropriately front-loaded and sufficient for a simple tool.

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

Completeness3/5

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

Given the tool has two parameters, one required, and no annotations, the description could be more complete. It fails to mention 'subject_segment' and does not describe the return value despite having an output schema.

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?

Schema description coverage is 0%, so the description must compensate. It links 'current_mastery_level' to the student's mastery level, but does not explain 'subject_segment', a required parameter. Partial compensation.

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 it retrieves a lesson plan based on mastery level, using specific verbs and resource. It distinguishes from siblings which involve calculations, assessments, or sync operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like calculate_spaced_repetition or generate_skills_assessment. The description does not provide context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

sync_ai_platform_stateC

AI Platform Connector: Verifies and synchronizes student progress with external platforms.

ParametersJSON Schema

Name	Required	Description	Default
`student_id`	Yes
`current_level`	Yes
`interval_days`	Yes
`platform_token`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It mentions 'verifies and synchronizes', implying mutation, but does not disclose side effects, auth requirements, or error conditions. The description is insufficient for an AI to anticipate behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, but it lacks substance. It front-loads the resource but fails to provide necessary details, making it under-specified rather than concise.

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?

Given the four parameters, three required, and no annotations, the description is inadequate. An output schema exists, so return values need not be described, but the description still fails to cover parameter usage, triggers, or limitations.

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

Parameters1/5

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

Schema description coverage is 0%, and the description does not explain any of the four parameters (student_id, current_level, interval_days, platform_token). No meaning beyond the schema's type and title is added.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('verifies and synchronizes') and resource ('student progress with external platforms'). However, it does not differentiate from sibling tools like 'sync_lms_onboarding_state', which share a similar purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives such as 'calculate_spaced_repetition' or 'sync_lms_onboarding_state'. The description lacks explicit context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

sync_lms_onboarding_stateC

LMS Sync Bridge: Integrates with Moodle/Canvas LTI services. Parses raw SCORM quiz scores, executes SM-2 scheduling, and outputs a signed LTI payload.

ParametersJSON Schema

Name	Required	Description	Default
`lms_student_id`	Yes
`quiz_score_pct`	Yes
`repetition_count`	Yes
`scorm_package_id`	Yes
`previous_ease_factor`	Yes
`previous_interval_days`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

C2.9/5.0

Behavior2/5

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

No annotations provided, so description must disclose all behavioral traits. It mentions parsing, scheduling, and payload generation but does not indicate side effects, authentication needs, or whether state is mutated.

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?

Two sentences with no extraneous words. However, for a complex tool, a bulleted list or structured overview could improve readability.

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?

Despite having an output schema, the description lacks prerequisites, error handling, and return value summary. Six required parameters demand more context for correct invocation.

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 has 6 parameters with 0% description coverage. The description only loosely maps quiz_score_pct to 'raw SCORM quiz scores' and implies SM-2 parameters but does not explain lms_student_id, scorm_package_id, or others.

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 it integrates with Moodle/Canvas, parses SCORM scores, executes SM-2 scheduling, and outputs a signed LTI payload. This distinguishes it from siblings like calculate_spaced_repetition and sync_ai_platform_state.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description provides high-level functionality but lacks conditions for use or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesv1.0.5Jul 18, 2026
- First observedcalculate_spaced_repetition
- First observedgenerate_skills_assessment
- First observedget_progressive_syllabus
- First observedsync_ai_platform_state
- First observedsync_lms_onboarding_state

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

B3.2/5.0

Scored 2026-07-20 23:31 across 5 tools

Disambiguation4/5

Most tools have distinct purposes: scheduling, assessment, syllabus, and syncing. However, 'calculate_spaced_repetition' and 'sync_lms_onboarding_state' both involve SM-2 scheduling, creating potential overlap and confusion for an agent.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with underscores (e.g., calculate_spaced_repetition, sync_lms_onboarding_state), making it predictable and easy to understand.

Tool Count5/5

With 5 tools, the server is well-scoped for a learning orchestrator. Each tool addresses a core need without unnecessary extras, fitting a typical range of 3-15 tools.

Completeness3/5

The set covers scheduling, assessment, syllabus retrieval, and external syncing, but lacks direct tools for student profile management or manual progress updates, which could lead to agent dead-ends.

Maintenance

ActivityStale

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Related MCP Connectors

UnifAPIOAuth
com.unifapi
Hosted MCP server for live public-data APIs and Skills for AI agents.
Open Data Search AI & Machine Learning
2
mcp-agentcore A
com.geiant
AI governance MCP server for EU AI Act compliance and jurisdiction verification
Legal & Compliance AI & Machine Learning Government Data
32
Tempreon — Personal AI Memory
com.tempreon
Person-owned AI memory that learns, not just stores — portable context for any MCP client.
Knowledge & Memory Autonomous Agents
2
H-Index A
ai.xr-utilities
Capability registry for the agentic economy. Semantic search over verified MCP server listings.
Search Documentation Access
2

Related MCP Servers

Knowledge Graph MCP Server
Education & Learning Tools Knowledge & Memory
zcsabbagh
A
license
B
quality
D
maintenance
An MCP server for tracking student learning via a knowledge graph, with spaced repetition scheduling and intelligent queries to identify gaps, misconceptions, and optimal next steps.
Updated 9 months ago (2025-12-30 05:24 UTC)
7
1
MIT
interactive-edtech-mcp
Education & Learning Tools Content Management Systems
FW1201
A
license
Not graded
quality
B
maintenance
MCP server suite that lets coding agents generate deep instructional content from external sources and operate classroom-interactive EdTech platforms including Padlet, Google Classroom, Kahoot!, Wayground, Wordwall, and Nearpod.
Updated 2 months ago (2026-07-07 12:40 UTC)
MIT
primer
Education & Learning Tools Knowledge & Memory
VedSoni-dev
A
license
Not graded
quality
A
maintenance
An MCP server that provides an open, local-first record of a child's learning, enabling an AI tutor to read, teach from, and update the child's knowledge, interests, and progress.
Updated a month ago (2026-08-05 22:15 UTC)
1
Apache 2.0
edumints SCORM MCP
Education & Learning Tools Content Management Systems
kemalyy
A
license
Not graded
quality
A
maintenance
An MCP server that compiles interactive, standards-conformant e-learning courses into SCORM packages. It lets you (or an AI client) describe a course as a structured spec and receive a self-contained SCORM zip ready for any LMS.
Updated 11 days ago (2026-09-09 03:54 UTC)
6
MIT

Appeared in Searches

A search for teaching and education resources