Enables code execution and testing on x86_64 architectures, specifically supporting AMD processors.
Provides support for running and testing code on Apple Silicon (ARM64) architectures.
Supports code execution across multiple ARM architectures, including ARM64 and ARM32.
Enables code execution and testing on x86_64 architectures, specifically supporting Intel processors.
Allows for code execution and testing environments targeting Raspberry Pi hardware.
Provides tools for the simulation and execution of code on RISC-V 64 architectures.
Supports the generation and execution of Rust code across various hardware architectures.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RespCode MCP Server Generate a Fibonacci function in Rust and run it on ARM64"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Multi-architecture code execution for Claude Desktop via MCP.
pip install respcode-mcpRelated MCP server: Claude Jester MCP
Get your API key at respcode.com
Configure Claude Desktop (~/.config/claude/claude_desktop_config.json):
{
"mcpServers": {
"respcode": {
"command": "respcode-mcp",
"env": {
"RESPCODE_API_KEY": "your-api-key-here"
}
}
}
}Restart Claude Desktop
Tool | Description | Credits |
| AI generates & executes code | 2-6 |
| Run your own code | 1 |
| Compare 4 AI models | ~15 |
| Models refine each other | ~12 |
| Best-of-4 selection | ~15 |
| View past prompts | 0 |
| Check balance | 0 |
x86_64 (Intel/AMD)
ARM64 (Apple Silicon, Raspberry Pi)
RISC-V 64
ARM32
Verilog/VHDL simulation
"Generate a fibonacci function in Rust and run it on ARM64"
"Compare all 4 AI models writing quicksort in C"
"Execute this code on RISC-V: print('Hello RISC-V')"
Website: https://respcode.com
Models work together: first generates, others refine, then execute final result.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What code to generate | |
| architecture | No | x86 | |
| models | No | 2-4 models in order (default: deepseek → claude) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes a multi-step process (generate, refine, execute) but doesn't specify what 'execute' means in practice, whether this involves external systems, what happens if refinement fails, or what the output format might be. For a tool with 3 parameters and no annotation coverage, this leaves significant behavioral questions unanswered.
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 communicates the core workflow. It's appropriately sized for the tool's apparent complexity. However, it could be more front-loaded with the primary purpose before detailing the process, and the phrase 'final result' is somewhat redundant with 'execute'.
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 3 parameters, no annotations, no output schema, and sibling tools that suggest this is part of a code generation/execution system, the description is incomplete. It doesn't explain what type of code is generated/executed, what 'execute' entails (compilation? running?), or how results are returned. The description leaves too many contextual questions unanswered for effective tool selection and use.
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 67% (2 of 3 parameters have descriptions), so the baseline is 3. The description adds no additional parameter information beyond what's in the schema - it doesn't explain the relationship between 'prompt', 'architecture', and 'models' parameters or how they affect the collaboration workflow. The description mentions models working together but doesn't elaborate on the 'models' parameter beyond what the schema already states.
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 'Models work together: first generates, others refine, then execute final result' which provides a high-level workflow but lacks specificity about what resource is being acted upon. It mentions a multi-model collaboration process but doesn't clearly distinguish this from sibling tools like 'generate', 'execute', or 'consensus' that might involve similar concepts. The purpose is understandable but vague about the exact outcome.
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 no guidance on when to use this tool versus alternatives like 'generate', 'execute', or 'consensus'. It describes a workflow but doesn't specify appropriate contexts, prerequisites, or exclusions. There's no mention of when this collaborative approach is preferred over simpler single-model tools or other multi-model approaches available in the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate with ALL 4 AI models and execute each. Compare which produces best code!
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What code to generate | |
| architecture | No | x86 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions generating with 4 models and comparing results, but doesn't specify which models are used, how execution works, what comparison criteria are applied, whether there are rate limits, or what the output format looks like. This leaves significant gaps for a tool that performs multiple operations.
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 extremely concise - a single sentence that efficiently communicates the core functionality. Every word earns its place with no wasted text, making it front-loaded and easy to parse.
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 tool that generates code with 4 different AI models, executes each, and compares results, the description is insufficient. With no annotations, no output schema, and incomplete parameter documentation, it lacks critical information about model identities, execution environment, comparison methodology, error handling, and output format.
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 50% (only the 'prompt' parameter has a description). The description doesn't mention any parameters, so it adds no semantic information beyond what the schema provides. The baseline is 3 since the schema covers half the parameters, but the description doesn't compensate for the undocumented 'architecture' parameter.
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 clearly states the tool's purpose: generate code with 4 AI models, execute each, and compare results. It specifies the verb ('generate with ALL 4 AI models and execute each') and resource ('code'), though it doesn't explicitly differentiate from sibling tools like 'generate' or 'execute'.
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 no guidance on when to use this tool versus alternatives like 'generate' or 'execute'. It states what the tool does but offers no context about appropriate use cases, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
All 4 models generate solutions, Claude picks/merges best one, then execute.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What code to generate | |
| architecture | No | x86 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the multi-model consensus and execution process, which is useful, but fails to mention critical traits like whether this is a read-only or destructive operation, authentication needs, rate limits, or what 'execute' entails (e.g., running code, side effects). For a tool with execution capabilities and no annotations, this is a significant gap.
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 extremely concise and front-loaded in a single sentence, with zero wasted words. Every element ('4 models generate', 'Claude picks/merges', 'then execute') directly contributes to understanding the tool's workflow, making it efficient and well-structured.
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's complexity (multi-model consensus with execution), lack of annotations, no output schema, and incomplete parameter documentation, the description is insufficient. It omits details on output format, error handling, execution consequences, and how parameters affect the process, leaving the agent with significant gaps for safe and effective use.
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 50% (one parameter has a description, one does not). The description adds no parameter-specific semantics beyond what the schema provides—it doesn't explain 'prompt' or 'architecture' usage, default behaviors, or how they influence the consensus process. With moderate schema coverage, the baseline 3 is appropriate as the description doesn't compensate for the coverage gap.
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 clearly states the tool's purpose: it generates solutions using 4 models, has Claude pick/merge the best one, and then executes. This is specific (verb+resource) and distinguishes it from siblings like 'execute' or 'generate' by mentioning the multi-model consensus process. However, it doesn't explicitly name the resource being acted upon (e.g., 'code' or 'solutions'), which prevents a perfect score.
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 no guidance on when to use this tool versus alternatives like 'generate', 'execute', or 'collaborate'. It implies usage for generating and executing code solutions, but lacks explicit when/when-not instructions or named alternatives, leaving the agent to infer context from sibling tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check your credit balance and see pricing.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read-only operation ('check', 'see'), but doesn't specify if authentication is required, rate limits apply, or what the output format might be. For a tool with zero annotation coverage, this is a significant gap in transparency.
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: 'Check your credit balance and see pricing.' It is front-loaded with the core purpose and has zero wasted words, making it highly concise and well-structured for quick understanding.
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's simplicity (0 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It states what the tool does but lacks details on behavioral aspects like authentication or output format. For a tool that likely involves sensitive data (credits/pricing), more context would improve completeness.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate. A baseline of 4 is applied since there are no parameters to document, and the description doesn't introduce unnecessary complexity.
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 clearly states the tool's purpose: 'Check your credit balance and see pricing.' It uses specific verbs ('check', 'see') and identifies the resource ('credit balance', 'pricing'). However, it doesn't explicitly distinguish this tool from its siblings (e.g., 'collaborate', 'generate'), which are likely unrelated to credit management, so it falls short of a perfect score.
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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for usage, or any exclusions. While the purpose is clear, the lack of usage instructions limits its helpfulness for an AI agent in selecting among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Execute YOUR code (no AI generation). Just run it on the sandbox. 1 credit.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Your source code to execute | |
| architecture | No | x86 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'no AI generation' and '1 credit', which adds useful context about limitations and cost. However, it lacks details on execution behavior such as time limits, resource constraints, error handling, or output format. For a code execution tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 extremely concise and front-loaded: it states the core purpose in the first phrase and adds critical constraints ('no AI generation', '1 credit') efficiently. Every sentence earns its place with no wasted words, making it easy for an agent to quickly grasp the tool's essence.
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 complexity of a code execution tool, no annotations, no output schema, and incomplete parameter documentation (50% schema coverage), the description is insufficient. It lacks details on execution environment, security implications, return values, and error cases. The mention of 'sandbox' and 'credit' provides some context, but overall, it doesn't provide enough information for safe and effective use.
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 50% (only the 'code' parameter has a description). The description adds no specific parameter semantics beyond what the schema provides—it doesn't explain the 'architecture' parameter or provide examples for 'code'. With low schema coverage, the description fails to compensate adequately, leaving half the parameters undocumented. The baseline is adjusted downward due to the coverage gap.
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 clearly states the tool's purpose: 'Execute YOUR code (no AI generation). Just run it on the sandbox.' It specifies the verb ('execute'), resource ('YOUR code'), and context ('sandbox'), distinguishing it from AI generation tools like 'generate'. However, it doesn't explicitly differentiate from sibling tools like 'rerun' or 'history', which might also involve code execution.
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 some usage context by stating 'no AI generation' and 'Just run it on the sandbox', implying this is for direct code execution rather than AI-assisted generation. However, it doesn't explicitly state when to use this tool versus alternatives like 'rerun' (which might re-execute previous code) or 'generate' (which involves AI). The mention of '1 credit' hints at a cost, but no explicit guidance on prerequisites or exclusions is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate code with ONE AI model and execute it. Default: deepseek-coder on x86.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What code to generate | |
| architecture | No | x86, arm64, riscv64, arm32, or verilog | x86 |
| model | No | AI model (claude, gpt4o, deepseek, gemini) | deepseek |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions code generation and execution but lacks critical details: whether this is a read-only or mutation operation, what permissions are required, how execution errors are handled, rate limits, or what the output format looks like. The description is insufficient for a tool that both generates and executes code.
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 extremely concise—just two sentences with zero wasted words. It's front-loaded with the core purpose and includes a useful default configuration. Every sentence earns its place.
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 complexity of a tool that both generates and executes code, with no annotations and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, error handling, or output format, leaving significant gaps for an AI agent to understand how to use this tool effectively.
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 100%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema—it mentions 'ONE AI model' and the default 'deepseek-coder on x86,' but doesn't explain parameter interactions or provide additional semantic context. This meets the baseline for high schema coverage.
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 clearly states the tool's purpose: 'Generate code with ONE AI model and execute it.' It specifies the verb ('generate'), resource ('code'), and scope ('execute it'), though it doesn't explicitly differentiate from sibling tools like 'execute' or 'rerun'.
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 no guidance on when to use this tool versus alternatives. It mentions a default configuration ('Default: deepseek-coder on x86') but offers no context about prerequisites, when to choose different models/architectures, or how it relates to sibling tools like 'execute' or 'rerun'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
View your recent prompts and execution results.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of items (max 50) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'View,' implying a read-only operation, but doesn't specify permissions, rate limits, or what 'recent' means (e.g., time range). This leaves gaps in understanding the tool's behavior beyond the basic purpose.
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 purpose without unnecessary words. It's appropriately sized for a simple tool with one parameter, making it easy for an agent to parse quickly.
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's low complexity (one optional parameter, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks context on usage, behavioral details, or output format, which could be improved for better agent guidance.
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 input schema has 100% description coverage, with the 'limit' parameter fully documented. The description doesn't add any parameter details beyond what the schema provides, such as explaining 'recent' or default behavior, so it meets the baseline for high schema coverage without extra value.
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 clearly states the tool's purpose with 'View your recent prompts and execution results,' which is a specific verb ('View') and resource ('prompts and execution results'). However, it doesn't distinguish this from sibling tools like 'history_search' or 'rerun,' which might have overlapping functionality, so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives such as 'history_search' or 'rerun.' It lacks explicit context, exclusions, or named alternatives, leaving the agent to infer usage from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search your prompt history by keyword.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search term | |
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool searches prompt history but doesn't describe what the search returns (e.g., format, scope), whether it's read-only or has side effects, or any limitations like rate limits. This leaves significant gaps in understanding how the tool behaves.
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 directly states the tool's function without unnecessary words. It's front-loaded with the core action, making it easy to parse quickly, which is ideal for 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 lack of annotations and output schema, the description is incomplete for a search tool with 2 parameters. It doesn't cover behavioral aspects like return format, error handling, or usage constraints, leaving the agent with insufficient information to use the tool effectively in complex scenarios.
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 50% (only 'query' has a description), and the description adds no additional parameter details beyond implying a keyword search. It doesn't explain the 'limit' parameter or provide context like search syntax, so it partially compensates but doesn't fully bridge the coverage gap, resulting in a baseline score.
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 clearly states the action ('Search') and the resource ('your prompt history by keyword'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'history' or 'rerun', which might also interact with prompt history, so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives like 'history' or 'rerun'. It mentions searching by keyword, but doesn't specify use cases, prerequisites, or exclusions, leaving the agent with minimal context for tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Re-run a previous prompt on a different architecture.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt_id | Yes | Prompt ID from history | |
| architecture | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the action is 'Re-run', implying a read-only or non-destructive operation, but doesn't clarify if it modifies data, requires permissions, has rate limits, or what the output entails. The description is minimal and lacks behavioral details beyond the basic action.
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, clear sentence with no wasted words. It front-loads the key action ('Re-run') and efficiently conveys the core functionality. Every part of the sentence contributes to understanding the tool's purpose.
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 no annotations, no output schema, and minimal parameter coverage, the description is incomplete. It doesn't address behavioral aspects like safety, output format, or error handling. For a tool with two parameters and potential complexity in re-running prompts across architectures, more context is needed to ensure proper agent usage.
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 50%, with one parameter ('architecture') having an enum and description, while 'prompt_id' lacks a description. The description adds no parameter-specific semantics beyond implying 'prompt_id' refers to history and 'architecture' is for re-running. It partially compensates for the coverage gap but doesn't fully explain parameter usage or constraints.
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 clearly states the verb ('Re-run') and resource ('a previous prompt'), making the purpose understandable. It specifies the action involves using a different architecture, which distinguishes it from simple re-execution. However, it doesn't explicitly differentiate from sibling tools like 'execute' or 'generate', which might have overlapping functionality.
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 no guidance on when to use this tool versus alternatives like 'execute' or 'generate'. It mentions 'previous prompt' and 'different architecture', implying context for re-running historical prompts with architectural changes, but lacks explicit when/when-not instructions or references to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Run, build, and validate firmware on virtual hardware from your AI agent. Hardware knowledge corpus.
Build Apps and run code in 30 languages — sandboxed, with persistent sessions for agent loops.
One workspace of tools for Claude and ChatGPT: connect 600+ apps, generate media, build tools.
Claude-powered AI tools: research, write, code, analyze, translate, debate, pitch, score, and more.
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