system_resources
Retrieve CPU, memory, and local disk metrics to assess system health and detect resource bottlenecks.
Instructions
Return CPU, memory, and local disk observations.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Retrieve CPU, memory, and local disk metrics to assess system health and detect resource bottlenecks.
Return CPU, memory, and local disk observations.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, but 'Return' strongly implies a read-only observation operation with no side effects. It does not disclose units, sampling behavior, potential permissions, or whether the data is real-time or cached, but for a simple zero-parameter read tool this is mostly adequate.
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 efficient sentence that front-loads the action and resource list. Every word earns its place, with no repetition of the tool name or schema contents.
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 zero-parameter read-only tool, the description offers enough to select and invoke it correctly. It identifies the exact resources observed, though it does not describe the shape, units, or format of the returned observations since no output schema is provided.
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 per the baseline rule there is nothing for the description to explain. The empty schema already fully documents the parameter surface, and the description correctly avoids inventing parameter details.
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 names a specific verb ('Return') and a specific resource set (CPU, memory, local disk), making the tool's scope clear. This also distinguishes it from sibling tools like network_summary and process_summary, which cover different resource categories.
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 use for retrieving system resource observations, and the sibling names suggest alternatives for process, network, and identity data. However, it does not explicitly state when to prefer this tool over alternatives or when not to use it.
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/313ON/CorporateMCP'
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