Execute a pipeline on a prompt. Steps run sequentially, each feeding its output to the next. Uses YOUR API keys.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The input prompt | |
| agent_id | Yes | The parent agent ID | |
| pipeline_id | Yes | The pipeline ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, openWorldHint=true), the description adds useful behavioral context by disclosing that steps run sequentially and that the tool consumes the caller's API keys. This clarifies that it makes external calls and may incur costs, which is valuable even with annotations present. It does not cover everything (e.g., return value, session creation) but adds meaningful transparency.
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 three concise sentences, front-loaded with the primary action, followed by the execution model and a key warning. Every sentence adds value with no unnecessary 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?
While the tool has only 3 parameters and helpful annotations, it lacks an output schema, and the description does not explain what the tool returns or how to retrieve results. The sequential execution and API key usage are explained, but the missing return-value information leaves a gap for the agent. Sibling tools like get_session hint at the outcome, but the description itself is incomplete.
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 coverage is 100%, with each parameter having a basic description. The tool description does not add significant semantics beyond what the schema already provides—it mentions 'prompt' as the input, but doesn't elaborate on how agent_id or pipeline_id are used. The baseline score of 3 is appropriate.
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 clearly states what the tool does: 'Execute a pipeline on a prompt.' It uses a specific verb and resource, and explains the sequential feeding behavior. This distinguishes it from sibling tools like kopern_create_pipeline or kopern_run_autoresearch by focusing on executing an existing pipeline on a prompt.
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 implies this tool is for running existing pipelines on prompts, which is clear from the name and first sentence, but it doesn't explicitly state when to use this over alternatives or mention any exclusions. The note about 'Uses YOUR API keys' provides a hint about prerequisites 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.
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.
