list_clustersList project clusters with descriptions and memory counts.
Use this to discover a cluster name before get_cluster. It does not return the memories stored inside each cluster.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| clusters | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Output schema / additionalPropertiesRemoved value: -trueOutput schema / propertiesAdded value: +{
+ "clusters": {
+ "items": {
+ "properties": {
+ "created_at": {
+ "type": "string"
+ },
+ "description": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "null"
+ }
+ ]
+ },
+ "is_default": {
+ "type": "boolean"
+ },
+ "memory_count": {
+ "anyOf": [
+ {
+ "type": "integer"
+ },
+ {
+ "type": "null"
+ }
+ ]
+ },
+ "name": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "name",
+ "description",
+ "is_default",
+ "created_at",
+ "memory_count"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ }
+}Output schema / requiredAdded value: +[
+ "clusters"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so no contradiction exists. The description adds value beyond those by specifying the return scope: descriptions and memory counts, not the memories themselves. This helps the agent predict behavior and avoid a mistaken follow-up.
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 two sentences with no filler. The first sentence states the core function and output, and the second provides essential usage context and a limitation. Every sentence earns its place, and key information is front-loaded.
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 has no parameters, a rich annotation set, and an output schema, the description is sufficiently complete. It tells the agent what the tool returns, what it does not return, and when to use it relative to get_cluster. No critical information is missing for correct invocation.
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?
The tool has zero parameters, and the input schema confirms no arguments are needed. With 0 parameters, the baseline is 4, and the description does not need to explain parameter semantics. The description's focus on output and usage 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 the tool's purpose: listing project clusters with descriptions and memory counts. It also distinguishes itself from get_cluster by explicitly noting it does not return the memories stored inside each cluster. This provides a specific verb and resource with clear differentiation from siblings.
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 gives direct usage guidance: use this tool to discover a cluster name before calling get_cluster. It also explains a key limitation, that it does not return cluster contents, so an agent knows not to use it for retrieving stored memories. This is explicit, actionable context for selecting the tool.
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.
Each tool has a clearly scoped purpose, and the descriptions include explicit usage rules that prevent overlap. Memory retrieval is split into read_context, search_memory, get_cluster, and export_all with distinct trigger conditions, so agents can reliably choose the right tool.
All tools use a consistent lower_snake_case verb_noun pattern such as list_clusters, get_skill, search_memory, write_observation, and upload_skill. The naming is predictable and uniform across the entire set.
Ten tools is well-scoped for a personal memory and skills server: it covers memory access, skill access, discovery, writing observations, and export. Each tool earns its place without the set feeling bloated or thin.
The server covers discovery, retrieval, saving observations, and uploading skills, but it lacks update/delete operations for memories and skill management beyond upload. This leaves a notable lifecycle gap: incorrect or obsolete saved observations cannot be removed or corrected in place.