get_knowledge_nodes

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

No annotations are present, so the description must carry the behavioral burden. It only implies a read operation, but says nothing about session_id validity, error behavior, pagination, or return structure, adding little beyond what the tool name already suggests.

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?

One short sentence, front-loaded with the core action and optional filter, with no filler. It is concise without being vague, though it omits necessary 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?

For a tool with a required session_id, no annotations, and no output schema, this is too sparse. The agent is not told what session_id refers to, what a knowledge node looks like, or what a 'teaching unit' should be in the filter.

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 phrase 'filtered by teaching unit' adds meaning to the unit_tag parameter beyond its title. However, session_id—the only required parameter—is never explained, and with 0% schema description coverage, the agent cannot infer what value to supply.

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 a clear verb ('List') and resource ('knowledge nodes'), and 'optionally filtered by teaching unit' defines scope. It distinguishes itself from sibling writer tools like save_knowledge_nodes by being a read/list operation, though it doesn't name a specific alternative.

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?

No explicit guidance on when to choose this over get_knowledge_edges, get_review_state, or other sibling tools. The optional-filter phrase gives a usage condition for unit_tag but not a decision rule for tool selection, so an agent must infer context from the name.

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