RespCode MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| RESPCODE_API_KEY | Yes | Your API key from respcode.com |
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": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generateC | Generate code with ONE AI model and execute it. Default: deepseek-coder on x86. |
| competeC | Generate with ALL 4 AI models and execute each. Compare which produces best code! |
| collaborateC | Models work together: first generates, others refine, then execute final result. |
| consensusC | All 4 models generate solutions, Claude picks/merges best one, then execute. |
| executeB | Execute YOUR code (no AI generation). Just run it on the sandbox. 1 credit. |
| historyB | View your recent prompts and execution results. |
| history_searchC | Search your prompt history by keyword. |
| rerunC | Re-run a previous prompt on a different architecture. |
| creditsB | Check your credit balance and see pricing. |
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 9 tools
There is significant overlap between the AI generation tools (collaborate, compete, consensus, generate), as they all involve generating and executing code with AI models, differing mainly in how models are combined. However, the descriptions help clarify the distinctions, and non-generation tools like credits, execute, history, history_search, and rerun have clear, non-overlapping purposes.
Most tools use a consistent verb-based naming pattern (e.g., collaborate, compete, generate, execute, rerun), which is readable and predictable. The only minor deviations are 'credits' (a noun) and 'history_search' (a compound word), but overall, the naming is largely consistent and follows a clear convention.
With 9 tools, the count is reasonable for a server focused on AI code generation and execution, covering generation variants, execution, history, and billing. It's slightly heavy due to multiple generation methods, but each tool has a defined role, making it well-scoped for the domain without being excessive.
The tool set covers core workflows for AI-powered code generation and execution, including multiple generation strategies, execution, history management, and billing. Minor gaps exist, such as no direct tool for editing or deleting history entries, but agents can likely work around this, and the surface is largely complete for the server's purpose.