react-perf-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| ANTHROPIC_API_KEY | Yes | Anthropic API key |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| review_codeA | Analyze a React/TypeScript code snippet for performance issues using parallel AI agents (re-renders, memoization, bundle size, profiling patterns) |
| review_fileA | Analyze a React/TypeScript file for performance issues by reading it from disk |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 2 tools
The two tools are clearly differentiated by input: one accepts a code snippet directly, the other reads a file from disk. While they share the same review purpose, the distinct input mechanisms prevent meaningful confusion.
Both tool names follow the consistent verb_noun pattern (review_code, review_file), making the naming predictable and easy to understand.
With only two tools, the server feels minimal for its intended scope. The count is borderline but acceptable for a focused performance-review utility, though it could benefit from additional entry points.
The tool surface covers two common input modes but lacks broader workflow support, such as analyzing a project directory, comparing branches, or generating reports. Agents may need to work around these missing capabilities.