获取 Prompt 列表
get_prompt_namesRetrieve a list of all available prompt names from the MCP server to select a template for task automation.
Instructions
获取所有可用的prompt名称
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_prompt_namesRetrieve a list of all available prompt names from the MCP server to select a template for task automation.
获取所有可用的prompt名称
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only states the action. It does not disclose behavioral traits such as read-only nature, side effects, or any prerequisites. The description adds no value beyond the tool's name.
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, front-loaded sentence that efficiently conveys the tool's purpose with no superfluous words. It 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?
Given the tool's simplicity (0 parameters, no output schema), the description is minimally adequate. However, it does not mention the format of the returned names or any ordering, leaving room for interpretation.
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% (0 parameters), so the description does not need to add parameter details. Baseline of 3 applies as the description provides no additional semantic context beyond the empty schema.
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 verb '获取' (get) and resource 'prompt名称' (prompt names), making the purpose specific. However, it does not differentiate from the sibling tool 'reload_prompts', which could cause ambiguity for the AI agent.
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 is provided on when to use this tool versus the sibling tool 'reload_prompts'. The description lacks any context about appropriate usage scenarios or conditions.
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/sayll/dev-prompt-mcp'
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