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get_discussion_entry_details

Retrieve detailed information about a specific Canvas discussion entry, including all replies, by providing course, topic, and entry identifiers.

Instructions

Get detailed information about a specific discussion entry including all its replies.

    Args:
        course_identifier: The Canvas course code (e.g., badm_554_120251_246794) or ID
        topic_id: The Canvas discussion topic ID
        entry_id: The Canvas discussion entry ID
        include_replies: Whether to fetch and include replies (default: True)
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_identifierYes
topic_idYes
entry_idYes
include_repliesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function implementing the 'get_discussion_entry_details' tool. It fetches a specific discussion entry's details from Canvas API using multiple endpoints (view, entry_list, entries, replies), handles fallbacks, anonymizes data, and formats a comprehensive response including content and replies.
    @validate_params
    async def get_discussion_entry_details(course_identifier: str | int,
                                         topic_id: str | int,
                                         entry_id: str | int,
                                         include_replies: bool = True) -> str:
        """Get detailed information about a specific discussion entry including all its replies.
    
        Args:
            course_identifier: The Canvas course code (e.g., badm_554_120251_246794) or ID
            topic_id: The Canvas discussion topic ID
            entry_id: The Canvas discussion entry ID
            include_replies: Whether to fetch and include replies (default: True)
        """
        course_id = await get_course_id(course_identifier)
    
        # Method 1: Try to get entry details from the discussion view endpoint
        entry_response = None
        replies = []
    
        try:
            # First try the discussion view endpoint which includes all entries
            view_response = await make_canvas_request(
                "get", f"/courses/{course_id}/discussion_topics/{topic_id}/view"
            )
    
            if "error" not in view_response and "view" in view_response:
                # Find our specific entry in the view
                for entry in view_response.get("view", []):
                    if str(entry.get("id")) == str(entry_id):
                        entry_response = entry
                        if include_replies:
                            replies = entry.get("replies", [])
                        break
        except Exception as e:
            log_warning(
                "Failed to fetch discussion view for entry details",
                exc=e,
                course_id=course_id,
                topic_id=topic_id,
                entry_id=entry_id
            )
    
        # Method 2: If view method failed, try the entry_list endpoint
        if not entry_response:
            try:
                entry_list_response = await make_canvas_request(
                    "get", f"/courses/{course_id}/discussion_topics/{topic_id}/entry_list",
                    params={"ids[]": entry_id}
                )
    
                if "error" not in entry_list_response and isinstance(entry_list_response, list):
                    if entry_list_response:
                        entry_response = entry_list_response[0]
            except Exception as e:
                log_warning(
                    "Failed to fetch entry from entry_list",
                    exc=e,
                    course_id=course_id,
                    topic_id=topic_id,
                    entry_id=entry_id
                )
    
        # Method 3: Fallback to getting all entries and finding our target
        if not entry_response:
            try:
                all_entries = await fetch_all_paginated_results(
                    f"/courses/{course_id}/discussion_topics/{topic_id}/entries",
                    {"per_page": 100}
                )
    
                if not isinstance(all_entries, dict) or "error" not in all_entries:
                    for entry in all_entries:
                        if str(entry.get("id")) == str(entry_id):
                            entry_response = entry
                            # Get recent_replies from this method
                            if include_replies:
                                replies = entry.get("recent_replies", [])
                            break
            except Exception as e:
                log_warning(
                    "Failed to fetch all entries as fallback",
                    exc=e,
                    course_id=course_id,
                    topic_id=topic_id,
                    entry_id=entry_id
                )
    
        # If we still don't have the entry, return error
        if not entry_response:
            return f"Error: Could not find discussion entry {entry_id} in topic {topic_id}. The entry may not exist or you may not have permission to view it."
    
        # Method 4: If we have the entry but no replies yet, try the replies endpoint
        if include_replies and not replies:
            try:
                replies_response = await fetch_all_paginated_results(
                    f"/courses/{course_id}/discussion_topics/{topic_id}/entries/{entry_id}/replies",
                    {"per_page": 100}
                )
    
                if not isinstance(replies_response, dict) or "error" not in replies_response:
                    replies = replies_response
            except Exception as e:
                log_warning(
                    "Failed to fetch entry replies from replies endpoint",
                    exc=e,
                    course_id=course_id,
                    topic_id=topic_id,
                    entry_id=entry_id
                )
    
        # Get topic details for context
        topic_response = await make_canvas_request(
            "get", f"/courses/{course_id}/discussion_topics/{topic_id}"
        )
    
        topic_title = "Unknown Topic"
        if "error" not in topic_response:
            topic_title = topic_response.get("title", "Unknown Topic")
    
        # Format the entry details
        course_display = await get_course_code(course_id) or course_identifier
    
        user_id = entry_response.get("user_id")
        user_name = entry_response.get("user_name", "Unknown user")
        message = entry_response.get("message", "")
        created_at = format_date(entry_response.get("created_at"))
        updated_at = format_date(entry_response.get("updated_at"))
        read_state = entry_response.get("read_state", "unknown")
    
        result = f"Discussion Entry Details for '{topic_title}' in Course {course_display}:\n\n"
        result += f"Topic ID: {topic_id}\n"
        result += f"Entry ID: {entry_id}\n"
        result += f"Author: {user_name} (ID: {user_id})\n"
        result += f"Posted: {created_at}\n"
    
        if updated_at != "N/A" and updated_at != created_at:
            result += f"Updated: {updated_at}\n"
    
        result += f"Read State: {read_state.title()}\n"
        result += f"\nContent:\n{message}\n"
    
        # Format replies
        if include_replies:
            if replies:
                result += f"\nReplies ({len(replies)}):\n"
                result += "=" * 50 + "\n"
    
                for i, reply in enumerate(replies, 1):
                    reply_id = reply.get("id")
                    reply_user_name = reply.get("user_name", "Unknown user")
                    reply_message = reply.get("message", "")
                    reply_created_at = format_date(reply.get("created_at"))
    
                    result += f"\nReply #{i}:\n"
                    result += f"Reply ID: {reply_id}\n"
                    result += f"Author: {reply_user_name}\n"
                    result += f"Posted: {reply_created_at}\n"
                    result += f"Content:\n{reply_message}\n"
            else:
                result += "\nNo replies found for this entry."
        else:
            result += "\n(Replies not included - set include_replies=True to fetch them)"
    
        return result
  • The registration of discussion tools, including 'get_discussion_entry_details', occurs here by calling register_discussion_tools(mcp) during server startup.
    register_discussion_tools(mcp)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that it 'gets' information, implying a read-only operation, but doesn't specify if it requires authentication, rate limits, error handling, or what the output looks like (though an output schema exists). 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.

Conciseness4/5

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

The description is front-loaded with the main purpose in the first sentence, followed by a structured Args section. It's appropriately sized with no wasted words, though the Args formatting could be more integrated into the flow. Every sentence adds value, making it efficient.

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 moderate complexity (4 parameters, no annotations, but with an output schema), the description is partially complete. It covers the purpose and parameters but lacks usage guidelines and behavioral context. The output schema mitigates the need to explain return values, but overall, it's adequate with clear gaps in guidance and transparency.

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 0%, so the description must compensate. It lists all four parameters with brief explanations (e.g., 'Canvas course code' for course_identifier, 'Whether to fetch and include replies' for include_replies), adding meaning beyond the schema's titles. However, it doesn't provide examples beyond a course code snippet or detail on ID formats, leaving some ambiguity.

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 'Get' and the resource 'detailed information about a specific discussion entry including all its replies.' It distinguishes from sibling tools like 'get_discussion_topic_details' and 'get_discussion_with_replies' by focusing on a single entry with replies. However, it doesn't explicitly differentiate from 'list_discussion_entries' beyond scope, which keeps it from 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 like 'get_discussion_topic_details' or 'list_discussion_entries.' It includes parameter details but lacks context about prerequisites, such as needing valid IDs or permissions, or when to prefer this over other discussion-related tools.

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

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