Get detailed results of a grading run: per-case scores, agent outputs, criteria evaluations, improvement notes. No LLM cost.
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes | The grading run ID | |
| agent_id | Yes | The agent ID or name | |
| suite_id | Yes | The grading suite ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotation readOnlyHint=true already declares a safe read operation. The description adds valuable behavioral context: 'No LLM cost' and the specific types of data returned (per-case scores, agent outputs, criteria evaluations, improvement notes), which goes beyond the annotation. No contradictions found.
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?
Two sentences with zero fluff. The first sentence front-loads the action and its result contents; the second adds a distinctive, useful note about cost. Every word 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?
With no output schema, the description adequately enumerates what the results contain. It also provides a behavioral note (no LLM cost) and implicitly ties to the run context via required parameters. This is complete for a retrieval tool with clear annotations and parameter schema.
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 100%, so the baseline is 3. The description does not add semantic detail to the parameters themselves; it only references the run context. The schema's short descriptions ('The grading run ID', etc.) are adequate, and the description does not need to compensate.
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 specific verb 'Get' and resource 'detailed results of a grading run', listing concrete contents (per-case scores, agent outputs, criteria evaluations, improvement notes). It clearly distinguishes from sibling tools like kopern_list_grading_runs by emphasizing 'detailed' results while not being a listing or run-triggering tool.
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 clearly implies this tool is for retrieving detailed results after a grading run, which is distinct from listing runs or running grading. However, it does not explicitly name alternative tools or give when-not-to-use guidance, so it falls short of a 5.
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.
Most tools have clearly distinct purposes (create vs list vs get vs run vs connect). A few potential overlaps exist (deploy_template vs create_agent, import_agent vs create_agent, grade_prompt vs run_grading) but descriptions clarify the differences.
All tools share the 'kopern_' prefix and mostly follow a verb_noun pattern (create_*, get_*, list_*, run_*, connect_*). The exception is 'kopern_compliance_report', which uses a noun phrase without a verb, breaking the otherwise consistent naming.
With 31 tools, this exceeds the 25-tool threshold for well-scoped servers. While the domain is broad (agent lifecycle, grading, pipelines, teams, connectors), the sheer number of tools feels heavy and could be consolidated (e.g., a single 'manage_memory' tool already bundles multiple actions).
Core agent management (create, read, update, delete, list) is solid, and grading has suite creation, execution, and results. However, pipelines and teams lack get/update/delete operations, connectors only support connect (no disconnect), and there's no way to manage grading suites beyond creation and running. This leaves notable gaps for secondary resources.
