Filter registered concepts by minimum confidence level to retrieve only those meeting your threshold for reliable context.
List registered concepts, filtered by minimum confidence.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | ||
| min_confidence | No | low |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.13.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description's mention of listing is consistent. It adds the filtering behavior (by minimum confidence), which is useful but does not disclose other behavioral traits like pagination, ordering, or scope (e.g., all users' concepts?).
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 a single, short sentence with no unnecessary words. It is front-loaded and efficient, earning 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?
Given the tool is simple (list with filter) and has an output schema, the description is largely complete. It could briefly mention that it returns a list of concepts or that 'k' limits results, but overall it provides sufficient context for an AI agent to understand basic behavior.
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. It explains 'min_confidence' (filtering), but does not mention 'k' (integer with default 10). Thus, it partially adds meaning but leaves one parameter unexplained.
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 the tool lists registered concepts with a specific filter (minimum confidence). It uses a specific verb ('List') and resource ('registered concepts'), and distinguishes from sibling tools like 'register_concept' or 'concept_manage' which perform other actions.
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 provides no guidance on when to use this tool versus alternatives. It does not mention when not to use it, prerequisites, or how it relates to other concept-related tools. The intended context is only implied by the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.