Returns ready-to-paste MCP configuration for Cursor, Claude Code, or Codex. Use on the Connect page or when setting up a new project.
| Name | Required | Description | Default |
|---|---|---|---|
| client | No | Target agent client. Omit to get all configs. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Returns' implies a safe, read-only operation and 'ready-to-paste' adds useful output-format context. However, it does not disclose auth requirements, error behavior, or output structure in a way that fully compensates for the missing annotations.
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?
Two sentences, 24 words, zero waste. The first sentence is front-loaded with the tool's core function and output, and the second adds targeted usage guidance.
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?
This is a simple tool: one optional parameter, no output schema, no annotations. The description covers what it returns, for which clients, and when to use it. The 'ready-to-paste' phrasing gives a sense of the output format. For a straightforward config getter, this is reasonably complete.
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% — the 'client' parameter already has an enum and description ('Target agent client. Omit to get all configs.'). The description mentions the three agent clients, reinforcing the enum, but adds little beyond what the schema already provides.
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 states a specific verb ('Returns') and a specific resource ('ready-to-paste MCP configuration') with explicit target clients (Cursor, Claude Code, or Codex). This clearly distinguishes it from all sibling tools, which focus on components, design, patterns, and deployment.
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 clear usage context: 'Use on the Connect page or when setting up a new project.' It does not explicitly name alternatives or state when not to use the tool, but for a config retrieval tool with clearly differentiated siblings, the context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but review_generated_code and validate_component_usage are very similar in functionality, differentiated only by intended usage context. list_components and search_components also have some overlap, though descriptions clarify their preferred use cases.
All tool names follow a consistent verb_noun snake_case pattern (e.g., get_component_info, list_components, review_generated_code). The verbs are clear and the naming convention is uniform throughout.
14 tools is well within the ideal 3-15 range and each tool serves a distinct role in the workflow: discovery, guidance, generation, validation, and prototype sharing. The count feels appropriate for the server's comprehensive purpose.
The server covers the full generation workflow: discover components, get guidelines, generate code, validate, and deploy/share. However, there is no tool to retrieve or list saved custom components, and prototype management is limited to deploy and feedback, leaving minor gaps in persisted resource handling.