lambda_list_instancesList your running GPU cloud instances (id, name, ip, status, region, instance_type, ssh_key_names, filesystems, jupyter_url). Lambda Cloud REST: GET /api/v1/instances.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint=true already establishes the read-only nature, so the description's added value is the returned field list and REST endpoint. It does not mention pagination, authentication, or potential side effects, and the word 'running' could mislead about the scope of results, but there is no direct contradiction with annotations.
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, front-loaded with the verb and resource, and includes the REST endpoint without redundancy. Every word earns 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?
For a no-parameter, read-only listing tool, the description covers the purpose and output fields well. However, it does not explicitly clarify that all instance statuses are returned, and the 'running' qualifier may under-specify the actual behavior, making the description slightly 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?
The tool has zero parameters, so the description carries no parameter-documentation burden. According to the rubric, a no-parameter tool receives a baseline of 4.
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 action (List) and resource (GPU cloud instances), and enumerates the returned fields, which distinguishes it from singular get_instance and other resource-specific list tools. However, the qualifier 'running' is slightly inaccurate or ambiguous relative to the referenced GET /api/v1/instances endpoint, which returns all instances.
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 is for listing instances as opposed to managing or retrieving a single one, but it provides no explicit guidance about when to prefer this over get_instance or other list tools. No exclusions or alternatives are mentioned, so usage context is only implied.
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 targets a distinct resource and action (e.g., list_instances vs. launch_instance vs. terminate_instances). There is no overlap or ambiguity between tools; even similar resources like firewall_rules and firewall_rulesets are clearly differentiated by name and description.
All tool names follow the verb_noun pattern with the lambda_ prefix, using lowercase and underscores consistently. The verb clearly indicates the operation (list, get, create, delete, launch, update, restart, terminate), and the noun indicates the resource. Minor pluralization differences (e.g., list_instances vs. get_instance) are conventional and do not break consistency.
15 tools is well-scoped for a GPU cloud management server, covering instances, filesystems, firewall rules, images, instance types, and audit events. Each tool serves a distinct purpose and no unnecessary duplication exists.
The instance lifecycle is well covered with launch, get, list, update, restart, and terminate. Filesystem and firewall read operations are present, but firewall rule/ruleset management (create/update/delete) is missing, which is a minor gap that may require working around via the dashboard.