Agentvet

Name: Agentvet
Author: MukundaKatta

by MukundaKattaversion 0.1.0i★ –iRuns on your computer

Registry nameio.github.MukundaKatta/agentvet

Install

Overview

Description from the maker (original English)

Validate tool-call args before execution. Returns LLM-friendly retry hints.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "agentvet": {
      "command": "npx",
      "args": [
        "-y",
        "@mukundakatta/agentvet-mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add agentvet -- npx -y @mukundakatta/agentvet-mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "agentvet": {
      "command": "npx",
      "args": [
        "-y",
        "@mukundakatta/agentvet-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "agentvet": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@mukundakatta/agentvet-mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account MukundaKatta, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5675 servers in this category
Published to the registry more recently than 11% of the other 5675 servers in this category
Basic checks equal to or better than 100% of the other 5675 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Agentvet safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.0) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

agentvet-mcp

MCP server for [`@mukundakatta/agentvet`](https://www.npmjs.com/package/@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-mcp

Three 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.

Add to your client

Claude Desktop

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.

Tool examples

`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.

Why a separate MCP server

@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).

Sibling MCP servers

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.*

License

MIT

Agentvet

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

agentvet-mcp

Add to your client

Claude Desktop

Tool examples

Why a separate MCP server

Sibling MCP servers

License