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Kopern

Kopern

by berch-t
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

kopern_list_agents

Read-only

List your AI agents with name, description, model, domain, and grading score at no LLM cost.

Instructions

List all your Kopern agents (name, description, model, domain, grading score). No LLM cost.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "kopern_list_agents"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.5

TDQS

A4.3/5.0
Behavior4/5

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

The readOnlyHint annotation already signals a safe read operation, and the description adds value by stating 'No LLM cost', which is a behavioral trait not covered by annotations. It also discloses the exact return fields, which is especially important given the lack of an output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is only two sentences long, front-loads the primary action and resource, and contains zero fluff. Every phrase—including the field list and cost note—contributes to comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a parameterless list tool with readOnlyHint annotations and no output schema, the description adequately explains what the tool returns and its cost profile. It is complete for the tool's apparent complexity, and the sibling tool names make the listing-vs-get distinction inferable even without explicit guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters and 100% schema coverage, the baseline is 4. The description adds contextual meaning by specifying 'your' agents, indicating an implicit ownership filter, and makes clear that no input is required. This provides sufficient semantic clarity beyond the empty schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'List' with the resource 'all your Kopern agents', clearly distinguishing it from the singular get_agent tool. It also enumerates the returned fields (name, description, model, domain, grading score), leaving no ambiguity about the tool's purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (to list all agents) and notes 'No LLM cost' as a selection benefit, but it does not explicitly state when not to use it or mention alternatives like get_agent for single-agent retrieval. Usage guidance is implicit rather than explicit.

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

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