@mukundakatta/agentvet용 MCP 서버. Claude Desktop, Cursor, Cline, Windsurf, Zed 또는 기타 MCP 클라이언트가 실행 전에 LLM이 생성한 도구 호출 인수를 검증하고, 문제가 발생했을 때 LLM 친화적인 재시도 메시지를 생성할 수 있도록 합니다.
npx -y @mukundakatta/agentvet-mcp세 가지 도구:
validate_tool_args — 작은 형태 사양(shape spec)에 따라 인수를 확인합니다. { valid, error?, retry_hint? }를 반환하며, 여기서 retry_hint는 LLM에 바로 보낼 수 있는 피드백 메시지입니다.
lint_tool_definition — LLM의 도구 사용 정확도를 떨어뜨리는 일반적인 실수가 있는지 도구 정의를 검사합니다.
generate_retry_message — 검증 오류가 주어지면, agentvet의 ToolArgError.toLLMFeedback() 형식을 사용하여 LLM 전용 재시도 메시지를 작성합니다.
~/Library/Application Support/Claude/claude_desktop_config.json(macOS) 또는 %APPDATA%\Claude\claude_desktop_config.json(Windows)을 편집하세요:
{
"mcpServers": {
"agentvet": {
"command": "npx",
"args": ["-y", "@mukundakatta/agentvet-mcp"]
}
}
}Cursor(~/.cursor/mcp.json), Cline, Windsurf, Zed도 동일한 형태입니다.
Related MCP server: Echo MCP
validate_tool_args:
{
"tool_name": "send_email",
"args": { "to": "a@b.com" },
"shape": { "to": "string", "subject": "string", "body": "string" }
}반환값:
{
"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" } } }
}
}non-snake_case 이름, 설명 누락, 필드 설명 누락, 필수 필드 없음 등에 대한 경고를 반환합니다.
generate_retry_message:
{
"tool_name": "send_email",
"validation_error": "missing required field: subject",
"attempted_args": { "to": "a@b.com" }
}런타임 호출자가 보는 표준 재시도 피드백 문자열을 반환하므로, 라이브 에이전트 루프 외부에서 재시도 텍스트를 준비할 수 있습니다.
@mukundakatta/agentvet은 의존성이 없는 JavaScript 라이브러리입니다. 이 MCP 서버는 해당 검증 기본 요소를 모든 MCP 지원 AI 어시스턴트에서 사용할 수 있게 해줍니다. 도구 레지스트리를 빠르게 감사하거나, 채팅을 나가지 않고도 어시스턴트에게 "이 인수 객체가 내 send_email 도구에 유효한가요?"라고 묻는 데 유용합니다.
에이전트 루프 내에서 런타임 인수 검증을 수행하려면 Node 프로세스 내부에서 @mukundakatta/agentvet을 직접 사용하세요(도구 함수를 래핑하고 ToolArgError를 동기적으로 발생시킵니다).
agent-stack 시리즈의 일부:
@mukundakatta/agentfit-mcp — Fit it.
@mukundakatta/agentguard-mcp — Sandbox it.
@mukundakatta/agentsnap-mcp — Test it.
@mukundakatta/agentvet-mcp — Vet it. (본 항목)
@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.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_name | Yes | ||
| validation_error | Yes | ||
| attempted_args | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes inputs and output but does not disclose behavioral traits like idempotency, side effects, or read-only nature. The tool appears to be a pure function, but this is not explicitly stated. Minimal transparency beyond functional description.
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 that are concise and front-loaded. Every sentence provides essential information without redundancy. The structure is clear and efficient.
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 simple string generation tool with 3 parameters and no output schema, the description covers inputs and the output's purpose. However, it does not describe the output format or provide examples, which would be helpful for an agent. Given the tool's simplicity and lack of annotations, the description could be more 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 0% (no parameter descriptions in schema). The description names the three parameters (tool_name, validation_error, attempted_args) and indicates their roles, but does not elaborate on expected formats, constraints, or examples. It adds value over the raw schema but does not fully compensate for the lack of schema descriptions.
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 specifies the tool's purpose: building a canonical LLM-facing retry feedback message given tool_name, validation_error, and attempted_args. It includes the formatting method (agentvet's ToolArgError.toLLMFeedback()), and distinguishes from siblings (lint_tool_definition, validate_tool_args) by its unique output.
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 implies when to use this tool—when generating a retry message after a validation error. It notes that the wording matches runtime callers, but does not explicitly state when not to use it or compare to alternatives. However, the siblings are sufficiently different, so no confusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | A tool definition: { name, description, inputSchema }. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes the analysis but does not explicitly state it is non-destructive or disclose output format. The behavior is implied but not fully transparent.
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, well-structured sentence with no unnecessary words. It efficiently communicates the tool's purpose and checks.
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?
No output schema is provided, and the description does not explain the return value (e.g., a list of issues). For a linting tool, this is a gap that reduces completeness for an AI agent.
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% and the parameter 'tool' has a description explaining its structure. The description adds context about what to include, aiding correct invocation.
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 specifies the verb 'Sanity-check' and the resource 'tool definition', listing specific checks. It distinguishes from sibling tools (generate_retry_message, validate_tool_args) by its focus on definition quality.
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 implies usage for checking tool definitions, but lacks explicit when-to-use or when-not-to-use guidance. However, the context of sibling tools helps differentiate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| tool_name | Yes | Name of the tool being called (surfaces in retry_hint). | |
| args | Yes | The args object the LLM wants to pass. | |
| shape | Yes | Shape spec mapping field name to type. Types: "string", "number", "boolean", "array", "object". Suffix with "?" for optional. Example: { "name": "string", "age": "number", "tags": "array", "notes": "string?" } |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description transparently states the return structure ({valid, error?, retry_hint?}) and explains that retry_hint is a ready-to-send LLM feedback message. This covers the behavior well, though it omits details like idempotency or side effects.
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?
A single sentence with a clear structure: first states the action, then lists the return fields. It is concise but could be improved by front-loading the most critical information (e.g., validation outcome).
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?
The description covers the basic function and return values, but lacks usage context like when to validate or how the shape spec works (already in schema). No output schema means description should be more detailed, but it adequately explains the return 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 coverage is 100% with detailed descriptions for each parameter. The description adds minimal extra meaning beyond the schema (e.g., clarifying retry_hint's purpose), meeting the baseline for high 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 uses a specific verb ('Validate') and resource ('tool-call args object against a small shape spec'), clearly distinguishing it from sibling tools (generate_retry_message, lint_tool_definition) which serve different purposes.
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?
No explicit guidance on when to use this tool versus alternatives. The description implies usage for validating tool-call arguments but does not provide prerequisites or contrasting scenarios with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0generate_retry_messagelint_tool_definitionvalidate_tool_argsScored 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.
MCP server for your apps' tools and custom tools, plus hosted AI agents and approval-gated workflows
MCP server providing access to the Scorecard API to evaluate and optimize LLM systems.
MCP server for progressive tool usage at any scale (see https://klavis.ai)
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
