Moodle MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation4/5
The tools are mostly distinct with clear purposes: get_assignments, get_quizzes, get_students, get_submissions, and get_submission_content target different resources, while get_quiz_grade and provide_feedback focus on specific actions. However, get_submissions and get_submission_content could be slightly confusing as they both relate to submissions, though their descriptions clarify the difference between listing submissions and getting detailed content.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, starting with 'get_' for retrieval operations and 'provide_' for the feedback action. This uniformity makes the tool set predictable and easy to understand, with no deviations in naming conventions.
Tool Count4/5
With 7 tools, the count is reasonable for a Moodle server focused on course management. It covers key areas like assignments, quizzes, students, and submissions, though it might be slightly thin for broader educational workflows, such as missing tools for creating or updating resources.
Completeness3/5
The tool set provides good read-only coverage for retrieving course data (assignments, quizzes, students, submissions) and includes a feedback action. However, there are notable gaps in CRUD operations: no tools for creating, updating, or deleting resources (e.g., assignments or quizzes), which limits agent functionality for full lifecycle management in the Moodle domain.
Average 3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves detailed content including text and attachments, which implies a read-only operation, but doesn't specify if it requires authentication, has rate limits, returns paginated results, or handles errors. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior beyond basic functionality.
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, efficient sentence in Spanish that front-loads the core purpose. It avoids redundancy and wastes no words, making it easy to parse. However, it could be slightly more structured by explicitly separating functionality from context, but this is minor.
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's complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It explains what the tool does but lacks details on output format (e.g., structure of returned content), error handling, or integration with siblings. Without annotations or output schema, more context on behavior would improve completeness, but it meets the basic threshold for a simple retrieval tool.
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?
The input schema has 100% description coverage, with clear descriptions for studentId and assignmentId. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.
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 purpose: 'Obtiene el contenido detallado de una entrega específica, incluyendo texto y archivos adjuntos' (Gets the detailed content of a specific submission, including text and attached files). It specifies the verb 'obtiene' (gets) and resource 'contenido de una entrega' (submission content), distinguishing it from sibling tools like get_submissions (likely listing submissions) or get_assignments (getting assignment details). However, it doesn't explicitly differentiate from all siblings, such as get_quiz_grade, which might also retrieve submission-related data.
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. It doesn't mention prerequisites, such as needing a specific submission ID or context from other tools, nor does it compare to siblings like get_submissions (which might list submissions without detailed content) or provide_feedback (which could involve submission content). Usage is implied by the description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states it retrieves submissions but doesn't describe what 'submissions' include (files, grades, comments?), whether it's paginated, requires authentication, has rate limits, or returns errors. The phrase 'en el curso configurado' implies some configuration context but doesn't explain how that works.
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, efficient Spanish sentence that directly states the tool's purpose. It's appropriately sized for a retrieval tool with documented parameters. However, it could be slightly more front-loaded by specifying it's a filtered retrieval operation rather than just 'gets submissions.'
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?
For a retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what constitutes a 'submission' (metadata, content, grades?), the return format, error conditions, or authentication requirements. The context of 'configured course' is mentioned but not explained. With 2 parameters and no structured output documentation, more completeness is needed.
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 100%, so the schema already fully documents both optional parameters (studentId and assignmentId) with clear descriptions of their optional nature and filtering behavior. The description adds no parameter-specific information beyond what's in the schema, so baseline 3 is appropriate.
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 action ('obtiene' - gets/retrieves) and resource ('entregas de tareas' - assignment submissions) in the configured course. It specifies the scope ('en el curso configurado') which helps distinguish it from general submission tools. However, it doesn't explicitly differentiate from sibling tools like get_submission_content or get_assignments.
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. It doesn't mention sibling tools like get_submission_content (which presumably retrieves submission details/content) or get_assignments (which retrieves assignment metadata). There's no indication of prerequisites, timing considerations, or use case boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('Proporciona feedback') but doesn't describe what this entails: whether it creates new feedback, updates existing feedback, requires specific permissions, has side effects (e.g., notifying the student), or what the response looks like. For a mutation tool with zero annotation coverage, this is a significant gap.
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, efficient sentence in Spanish that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every part contributing to understanding the action and target.
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 complexity (a mutation tool for providing feedback with grades), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like permissions, side effects, or response format, leaving gaps for an AI agent to correctly invoke the tool in context with siblings.
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 100%, with all parameters clearly documented in the input schema (studentId, assignmentId, grade, feedback). The description adds no additional meaning beyond what the schema provides, such as explaining relationships between parameters or usage examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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 verb 'Proporciona' (provides) and the resource 'feedback sobre una tarea entregada por un estudiante' (feedback on a task submitted by a student), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_submissions' or 'get_quiz_grade', which are read-only operations, while this appears to be a write operation.
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. It doesn't mention prerequisites (e.g., needing to check submission status first), exclusions (e.g., not for unsubmitted tasks), or comparisons to siblings like 'get_quiz_grade' (which might retrieve grades). Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral information. It states it retrieves a list but doesn't disclose pagination behavior, authentication requirements, rate limits, error conditions, or what 'curso configurado' means operationally. The description doesn't contradict annotations (none exist), but provides inadequate behavioral context.
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, efficient sentence in Spanish that states the core purpose. It's appropriately concise for a simple retrieval operation, though it could potentially be more front-loaded with key behavioral information given the lack of annotations.
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?
For a tool with no annotations, no output schema, and minimal description, this is incomplete. The description doesn't explain what format the student list returns, what fields are included, whether it's paginated, or how 'curso configurado' is determined. Given the complexity of student data retrieval, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, earning a baseline score of 4 for zero-parameter tools.
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 action ('obtiene' - gets/retrieves) and resource ('lista de estudiantes inscritos en el curso configurado' - list of students enrolled in the configured course). It's specific about what it retrieves, though it doesn't explicitly differentiate from sibling tools like get_assignments or get_submissions.
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 about when to use this tool versus alternatives. The description doesn't mention prerequisites, context for 'curso configurado' (configured course), or how this differs from other student-related operations that might exist in the system.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 states it 'gets' a grade, implying a read-only operation, but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what happens if the student or quiz doesn't exist. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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, clear sentence in Spanish that directly states the tool's purpose. It is front-loaded with no unnecessary words, making it highly concise and well-structured for quick understanding.
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's low complexity (2 required parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage guidelines, behavioral transparency, and output format. Without annotations or an output schema, more context would be helpful, but it's not severely incomplete.
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?
The schema description coverage is 100%, with both parameters (studentId and quizId) clearly documented in the schema. The description adds no additional meaning beyond implying these IDs are needed, so it meets the baseline of 3 where the schema does the heavy lifting without extra value from the description.
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 purpose: 'Obtiene la calificación de un estudiante en un quiz específico' (Gets a student's grade on a specific quiz). It specifies the verb (obtiene/gets) and resource (calificación/grade) with context (student, quiz). However, it doesn't explicitly differentiate from sibling tools like get_submissions or get_quizzes, which might also involve grades or quizzes.
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. It doesn't mention prerequisites, such as needing student and quiz IDs, or compare it to siblings like get_submissions (which might return grades among other data) or get_quizzes (which might list quizzes without grades). Usage is implied but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only states what the tool does ('obtiene la lista') but doesn't describe important behavioral aspects: whether this is a read-only operation, what format the list returns, whether there are pagination limits, authentication requirements, or error conditions. For a tool with zero annotation coverage, this is inadequate.
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, efficient Spanish sentence that directly states the tool's purpose with no wasted words. It's appropriately sized for a simple retrieval tool and gets straight to the point without unnecessary elaboration.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'curso configurado' means (is this a pre-configured course ID? current context?), what format the quiz list returns, or any limitations. For a tool that presumably returns data, the description should provide more context about the output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters (schema coverage 100%), so the description doesn't need to explain parameters. The baseline for zero parameters is 4. The description appropriately focuses on the tool's purpose rather than parameter documentation.
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 action ('Obtiene' - gets/retrieves) and the resource ('lista de quizzes en el curso configurado' - list of quizzes in the configured course). It distinguishes itself from siblings like get_assignments or get_quiz_grade by specifying it retrieves quizzes rather than assignments or grades. However, it doesn't specify whether this is a filtered list or complete list, which prevents a perfect score.
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. It doesn't mention prerequisites (like needing a configured course), whether it should be used before or after other tools, or how it differs from similar tools like get_assignments. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 states it 'obtiene' (gets/retrieves) which implies a read-only operation, but doesn't disclose any behavioral traits like whether it requires authentication, returns paginated results, includes filtering options, or has rate limits. For a tool with zero annotation coverage, this is a significant gap in behavioral context.
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, efficient sentence in Spanish that directly states what the tool does. It's front-loaded with the core action and resource, with no wasted words or unnecessary elaboration. Every part of the sentence contributes to understanding the tool's function.
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 0 parameters, no annotations, and no output schema, the description is minimally adequate. It explains what the tool retrieves (assigned tasks in a configured course), but lacks details on behavioral aspects, return format, or differentiation from siblings. For a simple read operation, it meets basic needs but leaves gaps in full context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the lack of parameters. The description doesn't need to add parameter semantics, and it correctly implies no inputs are required by not mentioning any. Baseline for 0 parameters is 4, as the description appropriately focuses on the tool's purpose rather than parameters.
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 action ('obtiene' - gets) and resource ('lista de tareas asignadas' - list of assigned tasks) with context ('en el curso configurado' - in the configured course). It's specific about what it retrieves but doesn't explicitly distinguish from sibling tools like get_submissions or get_quizzes, which might retrieve related but different resources.
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 like get_submissions or get_quizzes. It mentions 'en el curso configurado' which implies a prerequisite context of a configured course, but doesn't specify when this is the appropriate tool for task-related queries versus other sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![moodle-mcp-server MCP server](https://glama.ai/mcp/servers/peancor/moodle-mcp-server/badges/card.svg)](https://glama.ai/mcp/servers/peancor/moodle-mcp-server)

Score Badge

Copy to your README.md:

[![moodle-mcp-server MCP server](https://glama.ai/mcp/servers/peancor/moodle-mcp-server/badges/score.svg)](https://glama.ai/mcp/servers/peancor/moodle-mcp-server)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/peancor/moodle-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server