List conclusions
list_conclusionsRetrieve saved conclusions from durable memory, filtering by tags or namespace.
Instructions
List saved durable conclusions.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| limit | No | ||
| namespace | No |
list_conclusionsRetrieve saved conclusions from durable memory, filtering by tags or namespace.
List saved durable conclusions.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| limit | No | ||
| namespace | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.7Input schema / properties / namespaceAdded value: +{
+ "minLength": 1,
+ "type": "string"
+}Input schema / properties / tagsAdded value: +{
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description only says 'List', omitting any behavioral details like whether it returns all conclusions, pagination behavior, or side effects. Agent has to infer from name alone.
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?
Extremely terse at 3 words, it is under-specified rather than concise. A minimal description may omit critical information needed for correct usage.
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 no output schema, no annotations, and undocumented parameters, the description is insufficient. It only states the basic function, lacking details on how parameters affect output or what the response contains.
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 has 0% description coverage and the description adds no information about the three parameters (tags, limit, namespace). Agent must rely on parameter names, which are somewhat clear but lack usage context.
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?
Description clearly states the verb 'List' and resource 'saved durable conclusions', making the tool's purpose immediately understandable and distinct from siblings like create_conclusion.
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 guidance on when to use this tool versus siblings such as recall_memory or search_memory. The description does not provide any context for tool selection.
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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