analyze_caching
Inspect caching performance signals to diagnose issues and improve website speed.
Instructions
Analyze caching performance signals.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | No | ||
| pathname | No | / |
Inspect caching performance signals to diagnose issues and improve website speed.
Analyze caching performance signals.
| Name | Required | Description | Default |
|---|---|---|---|
| taskId | No | ||
| pathname | No | / |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must convey behavioral traits such as whether the tool is read-only, what it returns, or any side effects. It merely states 'Analyze caching performance signals' without explaining the output format, depth of analysis, or whether it modifies any state.
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, which is concise and front-loaded. However, it is too terse to be considered well-structured; it lacks any breakdown of how the analysis operates or what inputs are relevant, so it reads more as under-specification than deliberate conciseness.
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 two parameters and no output schema, the description leaves many gaps: it doesn't state what 'caching performance signals' means, what the tool produces, or how taskId and pathname influence the result. A more complete description would clarify these aspects.
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 description coverage is 0%, and the description provides zero information about the two parameters (taskId and pathname). It neither explains their purpose nor how they affect the analysis, making it impossible for an agent to know what values to supply.
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 uses a specific verb ('Analyze') and resource ('caching performance signals'), clearly indicating a caching-specific analysis. It distinguishes itself from sibling tools like analyze_images or analyze_fonts, though it overlaps conceptually with run_lighthouse_audit.
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 alternatives. There is no mention of preferred contexts or exclusions, leaving the agent to infer usage from the name alone.
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/rootellectecomm/rootellectmcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server