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blackboard_list_discussions

Retrieve Blackboard Ultra discussions for a course, showing titles and topic bodies, and filter by title or graded status to find relevant conversations.

Instructions

List Ultra discussions in a course, including titles and topic bodies when Blackboard exposes them. This is read-only and returns only discussions the current student can access.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum discussions to return (default 100)
titleNoOptional case-insensitive title search
offsetNoPagination offset
courseIdYesBlackboard course ID
gradableNoFilter to graded or ungraded discussions

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.1

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral disclosure burden. It explicitly states the operation is read-only and that only discussions accessible to the current student are returned, which are important behavioral traits. It also acknowledges that topic bodies may not always be exposed by Blackboard. It does not mention pagination details or rate limits, but those are less critical for a read-only list operation.

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

Conciseness5/5

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

The description is two sentences with no filler. The main action is front-loaded, and each sentence adds distinct value: output content, access constraints, and read-only behavior. It is appropriately sized for the tool's complexity.

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

Completeness4/5

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

The tool has five parameters and no output schema, but the parameters are fully described in the schema. The description gives the essential return information ('titles and topic bodies') and access scope. It does not describe the exact list item structure or sorting, but for a simple listing tool the current description is largely sufficient for selection and invocation.

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

Parameters3/5

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

Schema description coverage is 100%, so the input schema fully documents all five parameters. The description does not add parameter-level details, but it is not required to because the schema already handles that responsibility. This aligns with the baseline of 3.

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 uses a specific verb and resource: 'List Ultra discussions in a course'. It also clarifies the output content ('titles and topic bodies'). It does not explicitly differentiate from sibling tools like blackboard_get_discussion or blackboard_list_discussion_messages, though the listing-oriented wording makes the core purpose clear.

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

Usage Guidelines3/5

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

The description implies when to use the tool: when you need a list of course discussions. However, it provides no explicit guidance about when not to use it or when a sibling like blackboard_get_discussion or blackboard_list_discussion_messages would be more appropriate. The access caveat is useful context, but not a full usage guide.

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