glm_action_log
Query action logs with filters for action type, limit, and offset to track system operations.
Instructions
작업 로그 조회.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 조회할 로그 개수 | |
| offset | No | 시작 오프셋 | |
| action_filter | No | 액션 타입 필터 |
Query action logs with filters for action type, limit, and offset to track system operations.
작업 로그 조회.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | 조회할 로그 개수 | |
| offset | No | 시작 오프셋 | |
| action_filter | No | 액션 타입 필터 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose any behavioral traits such as being read-only, pagination behavior, or performance characteristics. It only states the basic operation.
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 wasted words, but being overly concise may omit necessary context.
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 and no annotations, the description fails to explain return values, pagination, or how to use the parameters effectively. The tool has 3 parameters but the description provides no context beyond the name.
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 coverage is 100% with parameter descriptions already present. The description adds no additional meaning beyond what the schema provides, so baseline 3 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 '작업 로그 조회.' (view action log) clearly states the verb and resource. However, among many sibling tools like glm_git_log and glm_memory_list, it does not differentiate itself explicitly.
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, no context on prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/coreline-ai/antigravity_glm_mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server