list_tracksList AI School course tracks, optionally filtered by a substring of the title or slug. Each track is a structured course on one subject. Use this to find the right course before drilling into its lessons.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 40, max 200) | |
| filter | No | Optional substring filter |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds useful context about substring matching on title/slug and the track concept, but does not disclose return formatting, ordering, or pagination behavior.
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 concise sentences with no filler. The main action and filtering behavior are front-loaded, the domain context is brief, and the usage guidance 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?
For a low-complexity, read-only list tool with two optional parameters, the description covers what the tool returns, how to filter, and when to use it. It lacks explicit return-shape detail, but 'List tracks' plus the schema-provided limit description keep it sufficiently 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 already documents both parameters at 100% coverage, so the baseline is 3. The description adds meaningful semantics by specifying that the filter applies to 'a substring of the title or slug,' going beyond the schema's generic 'Optional substring filter' text.
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 names the exact action and resource: 'List AI School course tracks' with optional substring filtering. It clearly distinguishes tracks from the sibling lesson tools by adding 'Each track is a structured course on one subject' and 'before drilling into its lessons.'
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 a clear usage context: 'Use this to find the right course before drilling into its lessons.' It implies this is for course discovery and that lesson-level tools are for later, but it does not explicitly name when not to use get_track or search_lessons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a clearly distinct entity and action: tracks are discovered and inspected via list_tracks/get_track, while lessons are searched and read via search_lessons/read_lesson. The descriptions explicitly guide when to use each tool, so there is no meaningful overlap or ambiguity.
All tool names follow a consistent verb_noun pattern with appropriate singular/plural usage: get_track, list_tracks, read_lesson, search_lessons. The naming style is uniform, predictable, and immediately conveys each tool's purpose.
Four tools is a well-scoped count for a read-only course content server: two for tracks and two for lessons. Each tool earns its place, and adding more would risk redundancy while removing any would create a discovery or reading gap.
The tool surface covers the full user journey for this domain: find a track, inspect its lesson list, search lessons across the curriculum, and read a lesson's full text. For a read-only educational content server, there are no obvious dead ends or missing operations.