codex_canvas_mcp
This is a local MCP server for reading and (with strict approval) authoring Canvas LMS content.
Read-only tools (available by default):
canvas_get_current_user– view authenticated Canvas user profile.canvas_list_modules– list modules and items in a course.canvas_read_api– GET any normalized Canvas API v1 path.canvas_get_write_policy– inspect local write-policy state.
Content-authoring tools (blocked by default; require per-course allowlist, exact confirmation phrase, and client approval):
Create/update pages, modules, module items, assignments, discussions, classic quizzes, and quiz questions.
Delete pages, assignments, discussions, classic quizzes, and modules (module deletion removes its item placements, not underlying content).
Image upload (blocked by default): upload a locally owned PNG/JPEG/WebP (≤10 MiB) from a configured trusted folder, with strict safety gates.
No generic Canvas write tool – all writes are specific and gated.
Credentials kept out of repo, fetched from 1Password and optionally macOS Keychain.
Provides tools for reading Canvas course content (user profile, modules, and API endpoints) and authoring pages, modules, module items, assignments, discussions, and Classic Quizzes after policy and approval checks.
Click on "Install 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., "@codex_canvas_mcplist the modules in my Canvas course 12345"
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.
Codex Canvas MCP
A portable local MCP server that lets compatible clients read Canvas and, only after several independent safety checks, author common course content.
Safety model
Canvas reads are available by default after local credential setup.
Writing is disabled by default.
Every content write requires an enabled local policy, an exact per-course allowlist match, exact confirmation text, and approval in the MCP client.
Image upload adds a payload gate: only a locally owned PNG, JPEG, or WebP inside one configured trusted folder, no larger than 10 MiB, can be sent.
There is no generic Canvas write tool.
Credentials stay in 1Password and, optionally, macOS Keychain. They do not belong in this repository or client configuration.
Every content tool is marked as mutating and destructive so compatible clients can require approval. The local server also rejects a call unless its exact confirmation matches the requested action, for example APPROVE CANVAS MODULE WRITE course <course_id>.
Rubric maintenance uses a separate operation-specific course allowlist and a stronger identity gate. canvas_delete_rubric requires APPROVE CANVAS RUBRIC DELETE course <course_id> rubric <rubric_id>, an exact expected title, course ownership, editable state, and an empty live used_locations result. Authorizing rubric deletion does not authorize any page, assignment, module, discussion, or quiz write. The tool returns the complete pre-deletion rubric definition and verifies that the rubric no longer appears in the active course list.
Rubric creation accepts a same-origin Canvas assignment URL, an assignment module-item URL, or a course URL containing an assignment_id query parameter. Query strings and fragments are ignored after the course and assignment are resolved. The URL never bypasses course policy: its course ID must still be explicitly allowlisted, and rubric creation requires APPROVE CANVAS RUBRIC WRITE course <course_id>. The tool refuses to replace an existing assignment rubric, requires descriptions for every criterion and rating, and requires grading-rubric points to match the assignment points.
Related MCP server: CoursePack Local MCP Server
What is included
Tool | Capability | Default |
| Read the authenticated profile | Available |
| Read modules and items for one course | Available |
| GET a normalized Canvas API v1 path | Available |
| Read one rubric, its complete definition, usage locations, and deletion preflight | Available |
| Inspect local policy state | Available |
| Upload one verified image from the configured trusted folder | Blocked |
| Create or update one page | Blocked |
| Create one module | Blocked |
| Place one content item in a module | Blocked |
| Create one assignment | Blocked |
| Create and attach one assignment rubric from a Canvas URL | Blocked |
| Create one discussion | Blocked |
| Create one Classic Quiz | Blocked |
| Add one question to a Classic Quiz | Blocked |
| Delete one page | Blocked |
| Delete one assignment | Blocked |
| Delete one discussion | Blocked |
| Delete one Classic Quiz | Blocked |
| Delete one module and its item placements | Blocked |
| Delete one exact, course-owned rubric only when Canvas reports zero usage locations | Blocked |
Set up a new Mac
Prerequisites: Python 3.11 or newer, the 1Password CLI, a 1Password service account that can read only the intended Canvas credential item, and a Canvas API token with the least privileges practical for your work.
Clone and install into an isolated environment:
git clone https://github.com/mrchris-ai/codex_canvas_mcp.git cd codex_canvas_mcp python3 -m venv .venv . .venv/bin/activate python -m pip install --upgrade pip python -m pip install -e .Create a 1Password item whose concealed field contains the Canvas API token. Give the service account read access only to the containing vault/item. Record the vault name, item name or ID, and concealed field label; do not put their values in this repository.
Choose one service-account-token delivery method:
For a short-lived shell session, provide
OP_SERVICE_ACCOUNT_TOKENto the MCP server process through your local secret launcher.On macOS, store that token in Keychain and configure non-secret lookup labels. Example command (it prompts securely; do not put the token on the command line):
security add-generic-password -U -s "your-service-label" -a "your-account-label" -w
Configure the server process with these non-secret values:
Variable
Purpose
CANVAS_BASE_URLCanvas HTTPS origin, such as
https://school.instructure.comCANVAS_OP_VAULT1Password vault name or ID
CANVAS_OP_ITEMCanvas credential item name or ID
CANVAS_OP_FIELDConcealed token field label; defaults to
credentialCANVAS_KEYCHAIN_SERVICEmacOS Keychain service label when not using
OP_SERVICE_ACCOUNT_TOKENCANVAS_KEYCHAIN_ACCOUNTmacOS Keychain account label when not using
OP_SERVICE_ACCOUNT_TOKENCANVAS_IMAGE_UPLOAD_ROOTAbsolute path to the only local folder from which image upload is allowed
Test only the protocol and security gates, without contacting Canvas or reading credentials:
python -m unittest discover -s tests -v printf '%s\n' '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | python -m canvas_mcpAdd the stdio server to Codex. The project-level form is:
[mcp_servers.canvas] command = "/absolute/path/to/codex_canvas_mcp/.venv/bin/python" args = ["-m", "canvas_mcp"] [mcp_servers.canvas.env] CANVAS_BASE_URL = "https://school.instructure.com" CANVAS_OP_VAULT = "your-vault" CANVAS_OP_ITEM = "your-item" CANVAS_OP_FIELD = "credential" CANVAS_KEYCHAIN_SERVICE = "your-service-label" CANVAS_KEYCHAIN_ACCOUNT = "your-account-label" CANVAS_IMAGE_UPLOAD_ROOT = "/absolute/path/to/reviewed/canvas-images"Put this in the trusted project's
.codex/config.tomlor your personal Codex configuration. Do not add secret values. Restart or open a new task after changing MCP configuration, then verify the listed tools before requesting Canvas data.
ChatGPT compatibility
This repository implements a local stdio MCP server for Codex and other clients that support local stdio MCP. Current ChatGPT custom apps do not connect directly to a local stdio server. OpenAI's current guidance is to expose a private local MCP through Secure MCP Tunnel (or deploy a reviewed remote MCP endpoint), subject to plan and workspace-admin availability. Do not expose this process directly to the public internet.
See Developer mode and MCP apps in ChatGPT before attempting ChatGPT setup; availability and approval behavior can change.
Enabling content-authoring tools
Keep writing off unless a specific task requires it.
Copy
config/write-policy.example.jsonto a location outside the repository.Add only the exact Canvas course IDs approved for content authoring to
approved_course_ids. Add courses approved only for zero-dependency rubric deletion toapproved_rubric_delete_course_ids; this does not enable other course writes.Set
enabledtotrueand restrict the file:chmod 600 /absolute/private/path/write-policy.jsonAdd
CANVAS_WRITE_POLICY=/absolute/private/path/write-policy.jsonto the MCP server environment.Restart the client and call
canvas_get_write_policyto verify the effective policy.For each content write, review the exact target and payload. Supply the action-specific confirmation phrase and approve the mutating action in the client.
Disable the policy again when the task is complete.
If the variable is absent, the file is missing, permissions are broader than 0600, writing is disabled, or the course is not allowlisted, the server refuses the write.
Enabling image upload
Image upload uses the same local write policy and exact course allowlist, plus CANVAS_IMAGE_UPLOAD_ROOT. The tool refuses relative paths, symlinks, files outside that root, files not owned by the current user, unsupported image types, files whose binary signature disagrees with the extension, and files larger than 10 MiB. The exact confirmation is APPROVE CANVAS IMAGE UPLOAD course <course_id>.
Canvas uses a documented three-step upload exchange: initialize the course file through the authenticated API, send the image without the Canvas access token to Canvas's returned HTTPS storage URL, and authenticate the returned same-origin Canvas completion location. The tool then reads /api/v1/courses/<course_id>/files/<file_id> and checks the MIME type and size before returning a persistent course preview path. It never returns storage signatures or the file-download verifier URL.
Current authoring boundary
This project is the source of truth for the shared local Canvas MCP used by supported chats on this Mac. It can author Pages, Modules and module items, Assignments and assignment Rubrics, Discussions, and Classic Quizzes with questions after the normal policy and approval gates. It also provides resource-specific deletion tools for Pages, Assignments, Discussions, Classic Quizzes, Modules, and zero-dependency course-owned Rubrics; deleting a Module removes its item placements but not the underlying course content. Its only local-file capability is the payload-gated image uploader described above. Generic file upload, New Quizzes, enrollments, cross-listing, provisioning, and account/course administration remain unsupported.
See docs/2026-08-06-content-authoring-extension.md for the implementation and end-to-end validation record.
See docs/2026-08-16-image-upload-extension.md for the image-upload threat model and validation record.
See docs/2026-08-24-support-maintenance-extension.md for the first support-maintenance tool contract and validation record.
Development
python -m pip install -e .
python -m unittest discover -s tests -v
python -m compileall -q src testsTests mock network and credential boundaries. See AGENTS.md for invariants contributors must preserve and ROADMAP.md for intentionally deferred administrative features.
Available Tools
11 toolscanvas_create_assignmentADestructive
Create exactly one Canvas assignment in an approved course. Disabled by default and requires explicit confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| course_id | Yes | ||
| published | No | ||
| description | No | ||
| confirmation | Yes | ||
| points_possible | No | ||
| submission_types | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond annotations: it notes the tool is 'disabled by default' and 'requires explicit confirmation,' which are important operational traits not captured by the readOnly/idempotent/destructive hints. It also clarifies the one-at-a-time creation behavior, providing useful 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 a single front-loaded sentence with no filler or repetition. Every word earns its place, making it highly concise and well-structured.
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 simplicity of the description, the tool has 7 parameters and no output schema, yet the description omits return value behavior and most parameter details. It does not explain what 'approved course' means or how confirmation works, leaving the agent without enough context to invoke it 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 description coverage is 0%, so the description must compensate, but it only references the 'confirmation' parameter. It does not explain the meaning or constraints of course_id, name, published, description, points_possible, or submission_types, leaving significant semantic gaps.
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 'Create exactly one Canvas assignment in an approved course,' specifying the verb (create), resource (Canvas assignment), and scope (exactly one, in an approved course). This distinguishes it from sibling creation tools like canvas_create_module or canvas_create_discussion.
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 implies usage when creating a Canvas assignment and mentions preconditions ('approved course', 'requires explicit confirmation'), but it does not explicitly name alternative tools or state when not to use this tool. The guidance is contextually clear but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_create_classic_quizADestructive
Create exactly one Canvas Classic Quiz in an approved course. New Quizzes are not supported by this tool. Disabled by default and requires explicit confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| course_id | Yes | ||
| published | No | ||
| quiz_type | No | practice_quiz | |
| description | No | ||
| confirmation | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as destructive (destructiveHint=true) and non-read-only. The description adds valuable context beyond this: the restriction to Classic Quizzes, the requirement for an approved course, and the need for explicit confirmation. It does not contradict the annotations and enriches behavioral understanding.
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 three sentences long, front-loaded with the main purpose, and each sentence adds distinct information (what it does, limitation, requirement). No filler or repetition; it is highly efficient.
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 complexity (6 params, no output schema), the description covers the core behavioral aspects: what is created, the Classic vs New distinction, and the confirmation requirement. It omits specifics about return values or quiz_type nuances, but these are secondary to the main purpose and reasonably supplemented by the schema.
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 coverage is 0%, so the description must compensate for parameter meanings. It only references the 'confirmation' parameter and indirectly references course_id via 'approved course', but leaves title, published, quiz_type, and description without any added context. This is insufficient for a tool with 6 parameters where the schema alone provides limited guidance.
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 action: 'Create exactly one Canvas Classic Quiz'. It specifies the resource type (Canvas Classic Quiz) with the verb 'create', and distinguishes from sibling tools by explicitly stating it is for Classic Quizzes, not New Quizzes, and from quiz_question by focusing on the quiz itself.
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 clear context: the tool works only in an 'approved course', is for Classic Quizzes (not New Quizzes), and 'requires explicit confirmation'. However, it does not explicitly name alternative tools for other quiz types or when not to use this tool, leaving a small gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_create_classic_quiz_questionADestructive
Add one question to an existing Canvas Classic Quiz in an approved course. Disabled by default and requires explicit confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| answers | No | ||
| quiz_id | Yes | ||
| course_id | Yes | ||
| confirmation | Yes | ||
| question_name | Yes | ||
| question_text | Yes | ||
| question_type | Yes | ||
| points_possible | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint: true, but the description adds crucial context by stating 'Disabled by default and requires explicit confirmation', which explains the need for a confirmation parameter. The mention of 'approved course' also signals authorization requirements, going beyond the annotation hints.
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 a single, dense sentence that conveys the core purpose and key constraints without waste. Every phrase adds value: 'Add one question', 'existing', 'approved course', and 'disabled by default' all contribute essential information.
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 8 parameters (6 required) and no output schema, this description is far too brief to provide adequate context. It fails to explain any parameter semantics beyond confirmation, the behavior of question_type enum, or what happens on success/failure, leaving the agent without sufficient guidance for correct invocation.
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%, and the description provides almost no parameter details. It only vaguely references 'explicit confirmation' (mapping to the confirmation parameter) and 'existing Canvas Classic Quiz' (implying quiz_id), but the other six required parameters (course_id, question_name, question_text, question_type, etc.) are left entirely to the schema, which lacks 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 'Add one question to an existing Canvas Classic Quiz in an approved course', specifying the verb (Add), resource (question), and target (existing quiz). This distinguishes it from siblings like canvas_create_classic_quiz, which creates a quiz, and other create_* 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 phrase 'to an existing Canvas Classic Quiz' provides clear context for when to use this tool (for adding questions to quizzes that already exist). It does not explicitly name alternatives or exclusions, but the 'existing' qualifier implies not for creating quizzes, which is handled by a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_create_discussionADestructive
Create exactly one Canvas discussion in an approved course. Disabled by default and requires explicit confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| message | Yes | ||
| course_id | Yes | ||
| published | No | ||
| confirmation | Yes | ||
| discussion_type | No | threaded |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as non-read-only and destructive, but the description adds that it is 'disabled by default' and 'requires explicit confirmation,' which are important behavioral constraints beyond the schema. This helps the agent understand prerequisites and that the tool will not execute without explicit user confirmation.
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 only two sentences, front-loading the primary purpose and immediately adding a critical constraint. Every word earns its place with no unnecessary detail, making it highly 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?
Given no output schema, the description does not explain return values or error behavior. It also leaves the 'confirmation' parameter value unspecified and does not clarify what 'approved course' means operationally. However, it does disclose the most critical gating (approval and confirmation), making it minimally adequate for a simple creation tool.
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 has 0% description coverage for all parameters, and the description only adds meaning to the 'confirmation' parameter by indicating it must be explicitly provided. It does not explain expected values for confirmation or clarify the semantics of title, message, course_id, published, or discussion_type, so it fails to compensate 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 'Create exactly one Canvas discussion in an approved course,' identifying the verb (create), resource (discussion), and scope (approved course). This distinguishes it from sibling tools that create modules, assignments, 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 clear context by noting the tool requires an 'approved course' and 'explicit confirmation,' setting prerequisites and conditions for use. However, it does not explicitly mention when to use this tool over alternatives or exclude other creation tools, so it lacks explicit usage comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_create_moduleADestructive
Create exactly one Canvas module in an approved course. Disabled by default and requires explicit confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| position | No | ||
| course_id | Yes | ||
| published | No | ||
| confirmation | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and destructiveHint=true, so the tool is known to be mutating. The description adds valuable context: it is 'disabled by default' and requires 'explicit confirmation', explaining why it might be unavailable. This goes beyond the annotations without contradicting them.
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 a single sentence that is direct and free of fluff. It front-loads the verb and resource, and every word adds meaning. No redundancy or unnecessary detail.
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 5 parameters, 3 required, and no output schema, so the description needs to cover return behavior, parameter details, and error states. It provides only minimal context (approved course, confirmation) and omits crucial information like success/failure behavior, response format, and implications of the position/published flags. This is insufficient for a create operation.
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 for documenting parameters. It only mentions 'explicit confirmation' (mapping to the confirmation parameter) and 'approved course' (vaguely relating to course_id), but does not explain name, position, published, or the format of confirmation. The description fails to provide essential parameter semantics.
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 creates exactly one Canvas module, using a specific verb ('create') and resource ('module'). This distinguishes it from sibling tools like canvas_create_module_item, which creates items within a module. The phrase 'in an approved course' adds scope, making the purpose 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 mentions prerequisites ('approved course') and a requirement for 'explicit confirmation', which implies the tool is for special cases and not a default action. However, it does not explicitly contrast with alternative tools (e.g., canvas_create_module_item) or state when not to use it. This is clear context but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_create_module_itemADestructive
Add one existing Canvas content item or external URL to an existing module in an approved course. Disabled by default and requires explicit confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | ||
| title | Yes | ||
| new_tab | No | ||
| page_url | No | ||
| position | No | ||
| course_id | Yes | ||
| module_id | Yes | ||
| content_id | No | ||
| confirmation | Yes | ||
| external_url | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a destructive, non-read-only operation. The description adds valuable context beyond annotations by specifying it is 'Disabled by default' and 'requires explicit confirmation', which are crucial behavioral traits. It also mentions the prerequisite of an approved course. This goes beyond simply restating the safety profile.
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 two concise sentences, front-loaded with the core purpose and containing no redundant information. It efficiently communicates the key action and the critical confirmation requirement.
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 10 parameters and no output schema, the description is too brief to be complete. It omits return value expectations, error conditions, or side effects beyond the destructive hint. It does not explain conditional parameter relationships (e.g., external_url vs content_id) or behavior when adding duplicates.
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 hints at the type parameter (content item vs external URL) and emphasizes the confirmation parameter. However, it does not clarify other parameters like position, new_tab, or page_url, leaving many fields semantically unexplained.
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's function: adding an existing Canvas content item or external URL to a module. It uses a specific verb ('Add') and resource ('module item'), and distinguishes from siblings like canvas_create_module by emphasizing the item already exists.
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 implies when to use this tool (for existing content items) but does not explicitly state when not to use it or name alternatives. It gives context about needing an approved course and explicit confirmation, but lacks explicit guidance on choosing between this and sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_get_current_userARead-only
Read the authenticated Canvas user's profile. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description repeats the readOnlyHint annotation by saying 'Read-only' but adds useful context by specifying 'authenticated user' scope. It does not disclose error handling, rate limits, or response format, which is acceptable given the annotation and simplicity.
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 two short sentences with no filler. It states the action and the read-only nature directly and front-loaded.
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 zero-parameter, read-only tool, the description is sufficient for basic understanding. It doesn't describe the return payload, but the simplicity and annotations compensate. A brief mention of returned fields would make it complete.
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 tool has no parameters, so schema coverage is 100% vacuously. Per the guidelines, a zero-parameter tool gets a baseline of 4, and the description adds no unnecessary parameter information.
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 'Read the authenticated Canvas user's profile' with a specific verb (read) and resource (authenticated user's profile). It distinguishes from sibling write tools like canvas_create_module or canvas_write_page.
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 intended usage is implied by the name and description – if you need the current user's profile, this is the tool. However, no explicit alternatives or exclusions are given, and it doesn't differentiate from canvas_read_api which could also be used for reading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_get_write_policyARead-only
Show write-policy state and approved course IDs. Does not expose credentials.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, and the description adds value by noting 'Does not expose credentials' and specifying the exact data returned. This goes beyond the annotation, providing safety context about what information is included or excluded.
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 two concise sentences, front-loaded with the core function and a useful safety note. Every word earns its place, and there is no redundant or ambiguous wording.
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 no-parameter, read-only getter without an output schema, the description fully covers what the tool does and what it returns. The additional credential-safety note adds helpful context, leaving no meaningful 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?
The tool has zero parameters, so the schema fully covers the input structure. Since no parameters exist, the description need not explain parameter semantics; the baseline of 4 applies.
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's function: showing write-policy state and approved course IDs. It uses a specific verb ('Show') and resource ('write-policy state'), distinguishing it from sibling tools that handle users, modules, or page operations.
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?
There is no guidance on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or compare with sibling tools like canvas_read_api or canvas_get_current_user, leaving the agent without explicit usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_list_modulesARead-only
List modules and module items for one Canvas course. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| course_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description reiterates 'Read-only,' adding little beyond that. It does add the scoping detail 'for one Canvas course,' which implies course_id targets a single course. However, it does not disclose behaviors like pagination, ordering, or what exactly is returned for modules/items. With annotations covering the safety profile, a score of 3 is appropriate.
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 immediately state the action and scope, and then note read-only behavior. No filler or redundancy. The information is front-loaded and every word earns its place.
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 (one parameter, read-only annotation, no output schema), the description covers the essential purpose, scope, and safety. It tells the agent it will get modules and module items for a single course. It lacks return format or pagination details, but for this level of complexity, the description is largely complete.
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 has one required parameter (course_id) with 0% description coverage. The description adds some meaning by stating 'for one Canvas course,' clarifying that course_id identifies the course and that the tool is scoped to a single course. It also indicates the output will include modules and module items. Yet it does not elaborate on parameter format or constraints beyond what the parameter name and schema already imply.
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 uses the specific verb 'List' with a clear resource ('modules and module items') and scope ('one Canvas course'). This clearly distinguishes it from sibling creation tools like canvas_create_module and canvas_create_module_item, as well as from the more general canvas_read_api.
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 implies usage (i.e., when you need to list modules/items for a course), and the 'Read-only' note hints it is safe for inspection. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or contrast with sibling tools. The guidance is largely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_read_apiBRead-only
Read one Canvas REST API v1 path with GET. Cannot make changes.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | ||
| query | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description reinforces this with 'Cannot make changes.' It adds the HTTP method (GET) and scope (one path), but does not disclose other behaviors like error handling, rate limits, or response format. With the annotation covering safety, this is adequate but not rich.
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 a single sentence, front-loaded with the core purpose, and every word earns its place. 'Read one Canvas REST API v1 path with GET. Cannot make changes.' is maximally concise and structurally clear.
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 is a generic reader with no output schema, so more context would help, such as mention of arbitrary path coverage or usage hints. The description is adequate for a simple read tool, but it leaves open questions about the query parameter and response shape, making it only a baseline complete.
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, but it only mentions 'path' without explaining its format or the optional 'query' object. The schema provides a pattern for path, but the description adds no semantic meaning for either parameter, especially query.
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 reads one Canvas REST API v1 path using GET, which is a specific verb and resource. It also explicitly says 'Cannot make changes,' distinguishing it from write tools. This satisfies the purpose clarity requirement.
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 what the tool does but gives no guidance on when to use it versus the specific sibling tools like canvas_get_current_user or canvas_list_modules. It does not mention alternatives or exclusions, so the agent receives no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
canvas_write_pageADestructive
Create or update exactly one Canvas page. Disabled by default. Requires a local per-course allowlist, exact user confirmation, and approval of this write action in the MCP client. This is not generic Canvas write access.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | ||
| title | Yes | ||
| page_url | No | Existing Canvas page URL slug. Omit to create a page. | |
| course_id | Yes | ||
| published | No | ||
| confirmation | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant context beyond the annotations: disabled by default, requires a per-course allowlist, exact user confirmation, and MCP client approval. This goes beyond the simple destructiveHint to explain how the destructive operation is gated and controlled.
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?
Three short sentences front-load the purpose first, then constraints, then a clarifying statement. Each sentence conveys essential information with no filler or 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?
The description covers the purpose, scope, and safety prerequisites, which are the most critical aspects for a gated write tool. It does not elaborate on parameter semantics, but the schema provides some names and defaults, and 'exactly one' sets expectation for behavioral scope.
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 only 17% (only page_url is described). The tool description does not explain the meaning of required parameters like confirmation, body, or course_id, and does not compensate for the low schema 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 a specific verb and resource: 'Create or update exactly one Canvas page.' It also adds scope ('exactly one') and explicitly distinguishes itself from generic write tools, which helps differentiate it from sibling write tools like canvas_create_module.
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 clearly indicates when to use this tool (for Canvas page creation/update) and explicitly excludes other write actions ('not generic Canvas write access'). It does not name specific alternatives, but the purpose and exclusions are unambiguous.
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. Dates show when Glama detected each change.
11 tool updates
v0.2.0- First observed
canvas_create_assignment - First observed
canvas_create_classic_quiz - First observed
canvas_create_classic_quiz_question - First observed
canvas_create_discussion - First observed
canvas_create_module - First observed
canvas_create_module_item - First observed
canvas_get_current_user - First observed
canvas_get_write_policy - First observed
canvas_list_modules - First observed
canvas_read_api - First observed
canvas_write_page
TDQS
Each tool targets a distinct Canvas resource and action: user info, module listing, generic API read, write policy, and specific create operations for pages, modules, module items, assignments, discussions, and quizzes. The quiz and quiz-question pair are clearly hierarchical, and the generic read tool is distinct from the specific list tool.
All tools use the 'canvas_' prefix followed by a verb_noun pattern (get_user, list_modules, create_assignment, etc.). Minor deviations include 'read_api' instead of 'get_api' and 'write_page' instead of 'create_page', but the overall pattern is predictable and consistent.
With 11 tools, the set is well-scoped for a Canvas LMS integration focused on reading course structure and creating content items. Each tool has a clear purpose, and the count falls comfortably in the ideal 3-15 range without feeling bloated.
The tool surface covers reading (user, modules, generic API), creation (pages, modules, module items, assignments, discussions, quizzes, quiz questions), and page updates, but lacks explicit update/delete operations for most created entities. The generic canvas_read_api provides a workaround for reads, but delete operations are entirely absent, leaving a noticeable lifecycle gap.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server for the Inistate platform: module discovery, entry management, and activity submission.
OAuth-protected, read-only-by-default MCP server for provenance-labeled QuillCaddie project memory.
Experimental MCP server for current empirical verification of explicit public HTTPS endpoint claims.
A MCP server built for developers enabling Git based project management with project and personal…
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA local MCP server for Canvas LMS that enables querying courses, assignments, grades, modules, and more from any MCP-compatible AI client.19MIT
- FlicenseNot gradedqualityAmaintenanceRead-only MCP server that provides Claude Desktop with access to converted Canvas course exports, including Markdown files, reports, and search tools for local course content.-
- AlicenseNot gradedqualityCmaintenanceA remote MCP server for querying Canvas LMS courses, assignments, and grades. Enables natural language interaction with Canvas data via MCP clients like Claude Desktop.19MIT
- AlicenseNot gradedqualityDmaintenanceMCP server for Canvas LMS with automatic OAuth authentication. Enables interaction with courses, assignments, grades, modules, discussions, quizzes, files, calendar, messaging, and more without manual API token management.159MIT
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/mrchris-ai/codex_canvas_mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server