Return the full ordered prerequisite chain for a concept — everything to learn first.
Args:
concept: Target concept — e.g. 'Speculative Decoding', 'Isaac Lab', 'NeMo Guardrails'.
domain: Domain name from list_domains().
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | ||
| concept | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / (root)Previous value: -{
- "properties": {
- "result": {
- "title": "Result",
- "type": "string"
- }
- },
- "required": [
- "result"
- ],
- "title": "get_prerequisitesOutput",
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden, and it does disclose the core behavior: it returns a full, ordered chain. 'Return' implies a read-only operation. It does not detail error handling, determinism, or whether the data is fetched versus local, but for a retrieval tool this is adequate disclosure.
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 compact: one purpose sentence plus a short Args block. Every element earns its place, and the key behavior is front-loaded. No filler 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?
With no output schema, the description should indicate what the tool returns — and it does: the full ordered prerequisite chain. It also handles parameter sourcing via list_domains(). It doesn't specify the exact return format or error behavior, but everything needed to call it correctly is present.
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 — and it does thoroughly. It provides concrete examples for 'concept' and tells the agent that 'domain' must come from list_domains(), which prevents invalid input. Both parameters are meaningfully explained beyond their bare schema definitions.
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 states a specific verb and resource: 'Return the full ordered prerequisite chain for a concept.' It also adds scope qualifiers ('full', 'ordered', 'everything to learn first') that make the tool's role immediately clear. This is more specific than a generic 'get prerequisites' phrasing.
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 gives clear context for when to use the tool — when you need the ordered prerequisite chain for a concept. It also instructs the agent to source the domain from list_domains(), which is useful operational guidance. However, it does not name alternatives or explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.