Click on "Deploy 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., "@agentvet-mcp Validate send_email args: to='a@b.com', missing subject"
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.
MCP server for @mukundakatta/agentvet. Lets Claude Desktop, Cursor, Cline, Windsurf, Zed, or any other MCP client validate LLM-generated tool-call args before execution and produce LLM-friendly retry messages when something's wrong.
npx -y @mukundakatta/agentvet-mcpThree tools:
validate_tool_args — check args against a small shape spec; returns { valid, error?, retry_hint? } where retry_hint is a ready-to-send LLM feedback message.
lint_tool_definition — sanity-check a tool definition for common mistakes that hurt LLM tool-use accuracy.
generate_retry_message — given a validation error, build the canonical LLM-facing retry message using agentvet's ToolArgError.toLLMFeedback() formatting.
Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"agentvet": {
"command": "npx",
"args": ["-y", "@mukundakatta/agentvet-mcp"]
}
}
}Same shape for Cursor (~/.cursor/mcp.json), Cline, Windsurf, Zed.
Related MCP server: Echo MCP
validate_tool_args:
{
"tool_name": "send_email",
"args": { "to": "a@b.com" },
"shape": { "to": "string", "subject": "string", "body": "string" }
}Returns:
{
"valid": false,
"error": "missing required field: subject",
"retry_hint": "send_email rejected your args: missing required field: subject. Please call again with the corrected arguments."
}lint_tool_definition:
{
"tool": {
"name": "BadName",
"inputSchema": { "type": "object", "properties": { "x": { "type": "string" } } }
}
}Returns warnings about non-snake_case name, missing description, missing field descriptions, and no required fields.
generate_retry_message:
{
"tool_name": "send_email",
"validation_error": "missing required field: subject",
"attempted_args": { "to": "a@b.com" }
}Returns the canonical retry feedback string the runtime callers see — so you can prepare retry text outside the live agent loop.
@mukundakatta/agentvet is a zero-dependency JavaScript library. This MCP server makes its validation primitives accessible from any MCP-aware AI assistant. Useful for quickly auditing a registry of tools, or asking the assistant "is this args object valid for my send_email tool?" without leaving the chat.
For runtime arg validation in your agent loop, use @mukundakatta/agentvet directly inside your Node process (it wraps your tool fn and throws ToolArgError synchronously).
Part of the agent-stack series:
@mukundakatta/agentfit-mcp — Fit it.
@mukundakatta/agentguard-mcp — Sandbox it.
@mukundakatta/agentsnap-mcp — Test it.
@mukundakatta/agentvet-mcp — Vet it. (this)
@mukundakatta/agentcast-mcp — Validate it.
MIT
Given a tool name, validation error, and attempted args, build the canonical LLM-facing retry feedback message. Uses agentvet's ToolArgError.toLLMFeedback() formatting so the wording matches what runtime callers see.
Inputs: tool_name (required), validation_error (required), attempted_args (required).
Sanity-check a tool definition for common mistakes that hurt LLM tool-use accuracy: missing description, vague description, no required fields, schema fields without descriptions, non-snake_case names.
Inputs: tool (required).
Validate a tool-call args object against a small shape spec. Returns { valid, error?, retry_hint? } where retry_hint is a ready-to-send LLM feedback message describing exactly what was wrong.
Inputs: tool_name (required), args (required), shape (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 3 tools
Each tool targets a distinct operation: generating retry messages, linting tool definitions, and validating tool args. There is no overlap in purpose.
All tool names follow a consistent verb_noun snake_case pattern (generate_retry_message, lint_tool_definition, validate_tool_args), making them predictable.
With 3 tools, the server is well-scoped for its purpose of agent vetting and validation. Each tool earns its place without being too sparse or bloated.
The set covers core validation, linting, and feedback generation. A minor gap is the absence of a tool to suggest fixes or re-lint after changes, but the core workflow is complete.
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MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
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Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
