Provides tools for interacting with Moodle Web Services, enabling AI agents to list courses, retrieve course contents, search courses, manage assignments, view upcoming events, get grades, and download files from a Moodle instance.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-moodle what's due this week?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that exposes Moodle Web Services to any MCP-compatible AI assistant — Claude Code, Claude Desktop, Cursor, Codex, and others.
Ask your assistant things like "what's due this week?", "list my courses", "download the slides from CS101 week 3" — without leaving the chat.
site_info — verify the token and get the authenticated user
list_my_courses — courses you're enrolled in
get_course_contents — sections, modules, file URLs
search_courses — search the public catalog
list_assignments — assignments across one or all courses
list_quizzes — quizzes/QCMs in one or all visible courses
get_quiz_qcm_content — rendered QCM questions, readable text, and images
upcoming_events — calendar deadlines and sessions
get_user_grades — your grades for a course
download_file — save any Moodle file locally (token appended automatically)
Works with any Moodle 3.5+ instance that has Web Services enabled.
Related MCP server: Moodle MCP Server
The recommended way is uv — no virtualenv to manage:
# One-off run (no install)
uvx mcp-moodle
# Or persist as a tool
uv tool install mcp-moodlePlain pip works too:
pip install mcp-moodleIf you installed with uv tool install, upgrade to the latest published
version with:
uv tool upgrade mcp-moodleIf you run it one-off with uvx, force a fresh fetch with:
uvx --refresh mcp-moodleFor pip installs:
pip install -U mcp-moodleMoodle Web Services require a personal token. The package ships a helper that handles every common login flow — native accounts, SSO (Microsoft, Google, SAML, OAuth), or manual paste:
# Default: opens a Chromium window, you complete SSO, token is captured
uvx --from "mcp-moodle[token]" mcp-moodle-token https://moodle.example.org
# Native (non-SSO) account
uvx --from "mcp-moodle[token]" mcp-moodle-token https://moodle.example.org \
--method local --user jdoe
# Headless server fallback (paste the moodlemobile:// URL by hand)
uvx --from "mcp-moodle[token]" mcp-moodle-token https://moodle.example.org \
--method manual-mobileThe token is written to ./.env (chmod 600) as MOODLE_URL and MOODLE_TOKEN.
Pass --stdout to print it to stdout instead.
The
[token]extra pulls in Playwright. First run downloads Chromium (~150 MB, one-time). Skip the extra if you only ever use--method local,--method web, or--method manual-mobile.
claude mcp add moodle \
--env MOODLE_URL=https://moodle.example.org \
--env MOODLE_TOKEN=your_token_here \
-- uvx mcp-moodleEdit ~/Library/Application Support/Claude/claude_desktop_config.json
(macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"moodle": {
"command": "uvx",
"args": ["mcp-moodle"],
"env": {
"MOODLE_URL": "https://moodle.example.org",
"MOODLE_TOKEN": "your_token_here"
}
}
}
}Any MCP client that supports stdio servers works the same way: command
uvx, args ["mcp-moodle"], env MOODLE_URL and MOODLE_TOKEN.
In your MCP client, ask: "call the moodle site_info tool". You should see your name, username, and the site URL.
Use list_quizzes to find quiz instance ids, then call get_quiz_qcm_content
with the quiz id. The tool reuses the latest unfinished attempt when one
exists. If no unfinished attempt exists, it returns
requires_attempt_creation: true instead of starting an attempt silently; ask
the user for permission, then call again with start_if_needed: true.
Each returned question keeps Moodle's raw html and also includes:
text — cleaned readable text with formula/image labels from alt/title
images — image metadata with the original url and a token-free
download_url that can be passed to download_file
download_file automatically converts Moodle pluginfile.php URLs to the
token-aware webservice/pluginfile.php form before downloading.
git clone git@github.com:Snaw80/moodle-mcp.git
cd moodle-mcp
uv sync --all-extras
uv run mcp-moodleYour token is the equivalent of a password for Moodle Web Services — keep
.env out of version control (the included .gitignore already does this).
The server reads MOODLE_TOKEN from the environment and never logs it.
download_file appends the token to the URL; that URL is not logged either,
but be mindful if your client echoes tool arguments.
MIT — see LICENSE.
Download a Moodle file (from get_course_contents) to a local path.
The Moodle token is appended automatically. The target directory is created if missing.
Args:
file_url: pluginfile.php URL from a module's contents[].fileurl
save_path: absolute local path to write the file to
| Name | Required | Description | Default |
|---|---|---|---|
| file_url | Yes | ||
| save_path | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses automatic token appending and directory creation, which are important behaviors. However, it does not mention overwrite policy, error handling, or file validation, leaving some uncertainty about edge cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise: three short sentences plus a parameter list. Every sentence adds value, and the structure is clear with no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations and output schema, the description covers the key aspects: source, destination, automatic token, and parameter details. It does not discuss return values or errors, but for a download tool, this level is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 0% but the description explains both parameters: file_url as a pluginfile.php URL from module contents and save_path as an absolute local path. This adds significant meaning beyond the bare schema names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool downloads a Moodle file to a local path, specifying the source as 'from get_course_contents' which ties to a sibling tool. The verb 'download' and the resource 'Moodle file' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use the tool ('from get_course_contents') and provides context about automatic token appending and directory creation. However, it does not explicitly state when not to use it or mention alternative tools, though no direct alternatives exist among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return sections, modules and file URLs for a course.
File URLs returned in contents[].fileurl require the Moodle token
appended as ?token=... (or use download_file).
Args: course_id: numeric course id (see list_my_courses)
| Name | Required | Description | Default |
|---|---|---|---|
| course_id | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses the token requirement for file URLs and mentions `download_file`, but does not discuss auth needs, rate limits, or side effects. This is adequate but not exhaustive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with exactly three sentences plus an Args list. It is front-loaded with the main purpose and avoids redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single required parameter and an output schema, the description fully explains the parameter and a key behavioral detail about file URLs. The agent has sufficient information to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema provides only type and title for `course_id` (0% description coverage). The description adds meaningful semantics: 'numeric course id (see list_my_courses)', which guides the agent on how to obtain the value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns sections, modules, and file URLs for a course, using a specific verb and resource. It differentiates from sibling `download_file` by mentioning the token requirement and suggesting that tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides clear usage context: file URLs need token appended, references `list_my_courses` for course IDs, and suggests `download_file` as an alternative. However, it does not explicitly exclude other cases or compare with other siblings like `search_courses`.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return rendered QCM/quiz questions from the current attempt.
The tool reuses the latest unfinished attempt. If no unfinished attempt
exists, it returns requires_attempt_creation=true unless
start_if_needed is explicitly true.
Args: quiz_id: quiz instance id (see list_quizzes) start_if_needed: create a new attempt only when no unfinished attempt exists preflight_data: optional Moodle preflight data, e.g. quiz password entries
| Name | Required | Description | Default |
|---|---|---|---|
| quiz_id | Yes | ||
| preflight_data | No | ||
| start_if_needed | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully covers behavioral traits. It discloses that the tool reuses the latest unfinished attempt, returns a flag if none exists, and that start_if_needed creates a new attempt only when needed. It also mentions optional preflight_data. It does not explicitly state if starting an attempt modifies state, but the conditions are well described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, front-loaded with the main purpose, and uses bullet points for parameters. Every sentence provides necessary information without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, no output schema, and no annotations. The description covers behavior, parameter semantics, and edge cases. It references list_quizzes for finding quiz_id. Missing explicit return format details, but overall complete for retrieval usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description provides all parameter meaning. It explains quiz_id references list_quizzes, start_if_needed creates a new attempt only when needed, and preflight_data is optional for preflight data like passwords. This adds significant value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns rendered QCM/quiz questions from the current attempt, specifying the verb 'return' and the resource. It distinguishes itself from sibling tools by focusing on the current unfinished attempt.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains that the tool reuses the latest unfinished attempt and provides behavior when none exists, including the requires_attempt_creation flag and the start_if_needed parameter. It lacks direct comparison with sibling tools but gives clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the authenticated user's grades for a course.
Args: course_id: numeric course id
| Name | Required | Description | Default |
|---|---|---|---|
| course_id | Yes |
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 states that it returns grades, but does not disclose authentication requirements, error handling, or performance characteristics. The mention of 'authenticated user' implies but does not explicitly address authorization.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with only two sentences and an Args section. However, the Args section is redundant with the schema and could be integrated into the prose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter, no output schema, and no annotations, the description is minimal. It lacks details about the return format, error scenarios, and what 'grades' encompasses (e.g., assignments, quizzes, final grade).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 repeats that course_id is a numeric course id, adding minimal meaning beyond the schema's type and title. No format, validation, or usage examples are provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Return' and the resource 'authenticated user's grades for a course', making the purpose unambiguous. It distinguishes from sibling tools like list_assignments and list_quizzes, which return different data types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives, such as when to use list_assignments instead. There are no exclusions or context about prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List assignments across given courses (or all enrolled if omitted).
Args: course_ids: optional list of course ids; omit to use all enrolled
| Name | Required | Description | Default |
|---|---|---|---|
| course_ids | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description should cover behavioral traits. It implies a read operation ('list') but does not disclose any details about output format, pagination, rate limits, or authentication requirements, leaving gaps in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences and front-loads the main action. However, it could be more structured with clear sections or bullet points, but it remains efficient with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the input parameter well but lacks any mention of the output format or return value, which is important for a list tool. Given the absence of an output schema and annotations, this is a notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful context to the course_ids parameter beyond the schema by explaining its purpose and default behavior (omit to use all enrolled). This compensates for the 0% schema description coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'list' and resource 'assignments', and specifies the scope 'across given courses (or all enrolled if omitted)', distinguishing it from sibling tools which cover different resources like files, courses, or quizzes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on the optional course_ids parameter and its default behavior, but does not explicitly state when to use this tool versus alternatives, nor does it mention any prerequisites or restrictions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List courses the authenticated user is enrolled in.
Returns id, shortname, fullname, category id, and visibility.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It states the action and return fields but omits details on authentication, pagination, errors, or whether hidden courses are included. It is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the primary action, followed by return field details. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, has output schema), the description is mostly complete. It could improve by mentioning sorting or limits, but it covers the core purpose adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist (schema coverage 100%), so the description does not need to add information. Baseline 4 applies as there is nothing to explain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'List courses the authenticated user is enrolled in' with a specific verb and resource. It also lists the return fields (id, shortname, etc.), making it distinct from the sibling tool search_courses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs alternatives like search_courses. The description implies its use for the user's own enrollments, but lacks explicit when-not or comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List quizzes/QCMs in given courses, or all visible quizzes if omitted.
Args: course_ids: optional list of course ids
| Name | Required | Description | Default |
|---|---|---|---|
| course_ids | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description provides minimal behavioral insight. It does not disclose whether the operation is read-only, whether it returns paginated results, or any side effects. For a simple listing tool, this is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: two sentences front-loading the purpose and parameter. No unnecessary words or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description could mention the expected return format (e.g., list of quiz objects). It does not address how the result relates to sibling tools. However, for a simple list operation with one optional parameter, it is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds essential meaning by explaining that course_ids is an optional list of course ids. This clarifies usage beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists quizzes/QCMs for given courses, with an optional filter. It distinguishes from siblings like 'get_quiz_qcm_content' which presumably retrieves a specific quiz's content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates when to use the course_ids parameter but does not explicitly contrast with alternatives such as 'get_quiz_qcm_content' or 'list_assignments'. Usage context is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the public course catalog by keyword.
Args: query: search text page: 0-indexed page perpage: results per page (max 100)
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| query | Yes | ||
| perpage | No |
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 describes the parameters (query, page, perpage) but does not disclose behavioral traits such as rate limits, behavior for empty queries, or default pagination behavior beyond the defaults in the schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence stating the purpose followed by a clear parameter list. Every sentence is informative and there is no redundancy or fluff. Front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and a simple search operation, the description covers the tool's purpose and parameters adequately. It could be improved by mentioning the return format (e.g., list of course objects) or any additional behavior, but it is sufficiently complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the tool description provides clear meanings for each parameter (e.g., 'query: search text', 'page: 0-indexed page', 'perpage: results per page (max 100)'). This adds value beyond the raw schema, though it lacks examples or constraints beyond max perpage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches the public course catalog by keyword, with a specific verb and resource. It distinguishes from siblings like list_my_courses (personal courses) and get_course_contents (within a course).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells what the tool does but does not explicitly state when to use it versus alternatives. For example, it does not mention that list_my_courses is for personal courses. Usage is implied but not clarified with exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return Moodle site info and the authenticated user's id/username.
Use this first to verify the token works.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavior. It discloses the return values but does not explicitly state read-only nature or other behavioral traits like rate limits or auth requirements beyond token verification.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the core purpose, no wasted words. Efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and no output schema, the description fully covers what it does and how to use it. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters, the schema has 100% coverage. The description adds value by explaining what the tool returns (site info and user id/username), going beyond the empty schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns Moodle site info and the authenticated user's id/username, which is a unique capability among sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using this tool first to verify the token works, providing clear usage context. However, it does not mention when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return upcoming calendar events (deadlines, sessions) for the user.
Args: limit: max number of events to return
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It mentions returning events 'for the user' but does not clarify time range, pagination beyond limit, or whether events are aggregated across calendars. The read-only nature is implied but not explicit.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise: one sentence for purpose and one line for the parameter. Every word serves a purpose with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the tool's simplicity, missing output schema means the description should describe the return structure (e.g., array of event objects with fields). It does not, leaving the agent without information on expected response format.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description adds essential meaning by stating that limit is the 'max number of events to return' and noting a default of 20. This compensates for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns upcoming calendar events, specifying types (deadlines, sessions) and user scope. 'Return' is a specific verb, and 'upcoming calendar events' is a distinct resource not overlapping with siblings like list_assignments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 (e.g., list_assignments for assignments as deadlines). Missing context such as prerequisites or filtering criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.2download_fileget_course_contentsget_quiz_qcm_contentget_user_gradeslist_assignmentslist_my_courseslist_quizzessearch_coursessite_infoupcoming_eventsScored across 10 tools
Every tool targets a distinct resource or action: file download, course contents, quiz content, grades, assignments, courses, quizzes, search, site info, and events. No two tools have overlapping purposes, and descriptions clearly differentiate them.
Most tools follow a consistent verb_noun pattern (e.g., download_file, get_course_contents, list_assignments). However, site_info and upcoming_events deviate from this pattern, breaking the otherwise consistent naming scheme.
With 10 tools covering core Moodle operations (listing courses, assignments, quizzes, retrieving content, grades, events, and file downloads), the count is well-scoped and neither too sparse nor overwhelming for the server's purpose.
The tool set covers read and listing operations well but lacks write actions such as submitting assignments, creating quizzes, enrolling in courses, or updating user data. This leaves notable gaps for interactive workflows, limiting completeness.
Connect your Moodle to AI assistants: courses, content, grading and reports from the chat.
Create, edit, translate, and export SCORM eLearning modules from a connected AI assistant.
Enable Large Language Model clients to interact seamlessly with any MediaWiki wiki. Perform action…
Secure MCP Server for WordPress connects AI assistants and agents to WordPress with secure, controlled access. It lets AI interact with WordPress through MCP while helping organizations manage access, enforce policies, protect non-human identities (NHI), and require human approval for sensitive actions. Use it to securely connect tools such as ChatGPT, Claude, and Cursor with WordPress. Marketplace Link: https://wordpress.org/plugins/miniorange-secure-mcp-server/ Official website: https://plugins.miniorange.com/mcp-server-ai-policy-enforcement-wordpress
