install-agentpmt-mcp
Install and configure the AgentPMT MCP server for any AI agent. Use this skill when the user wants to connect Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, OpenAI Codex CLI, Gemini CLI, or any MCP-compatible client to the AgentPMT tool marketplace. Also use when the user mentions AgentPMT setup, MCP server configuration, agent tool marketplace, or connecting an AI agent to paid tools and APIs.
Best use case
install-agentpmt-mcp is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Install and configure the AgentPMT MCP server for any AI agent. Use this skill when the user wants to connect Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, OpenAI Codex CLI, Gemini CLI, or any MCP-compatible client to the AgentPMT tool marketplace. Also use when the user mentions AgentPMT setup, MCP server configuration, agent tool marketplace, or connecting an AI agent to paid tools and APIs.
Teams using install-agentpmt-mcp should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/install-agentpmt-mcp/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How install-agentpmt-mcp Compares
| Feature / Agent | install-agentpmt-mcp | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Install and configure the AgentPMT MCP server for any AI agent. Use this skill when the user wants to connect Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, OpenAI Codex CLI, Gemini CLI, or any MCP-compatible client to the AgentPMT tool marketplace. Also use when the user mentions AgentPMT setup, MCP server configuration, agent tool marketplace, or connecting an AI agent to paid tools and APIs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# Install AgentPMT MCP Server
Connect any MCP-compatible AI agent to the AgentPMT tool marketplace. AgentPMT gives agents access to a dynamic catalog of tools, APIs, and services -- all controlled by budgets and spending limits.
## Prerequisites
The user needs an AgentPMT account with:
1. An **API Key** -- generated from Dashboard > Account > API Keys
2. A **Budget Key** -- generated from Dashboard > Budgets > [Select Budget] > Budget Keys
If the user does not have these, direct them to create an account at https://www.agentpmt.com and set up a budget first.
## Choose Installation Method
There are two ways to connect. Use the STDIO connector for desktop AI clients. Use the direct HTTPS endpoint for programmatic or web-based agents.
---
## Method 1: STDIO Connector (Recommended for Desktop Clients)
This method uses the `@agentpmt/mcp-router` package, a lightweight local connector that routes MCP traffic to the AgentPMT cloud. It does not access local files or execute anything on the user's machine.
### Automatic Setup
Run the interactive setup tool:
```bash
npm install -g @agentpmt/mcp-router
agentpmt-setup
```
The setup tool auto-detects installed AI platforms, prompts for credentials, writes the configuration files, and restarts the AI tools.
### Manual Setup
If automatic setup is not available or the user prefers manual configuration, follow the platform-specific instructions below.
#### Step 1: Generate the Bearer Token
Combine the API key and budget key, then base64-encode them:
```bash
echo -n "YOUR_API_KEY:YOUR_BUDGET_KEY" | base64
```
This produces the Bearer token used in all configurations below.
#### Step 2: Configure the AI Client
**Claude Desktop**
Edit the config file:
- macOS: `~/.config/Claude/claude_desktop_config.json`
- Windows: `%APPDATA%\Claude\claude_desktop_config.json`
```json
{
"mcpServers": {
"agentpmt": {
"command": "npx",
"args": ["--package=@agentpmt/mcp-router@latest", "agentpmt-router"],
"env": {
"AGENTPMT_BEARER_TOKEN": "<your-base64-token>"
}
}
}
}
```
**Claude Code**
Add to the project's `.mcp.json` or the global `~/.claude/mcp.json`:
```json
{
"mcpServers": {
"agentpmt": {
"command": "npx",
"args": ["--package=@agentpmt/mcp-router@latest", "agentpmt-router"],
"env": {
"AGENTPMT_BEARER_TOKEN": "<your-base64-token>"
}
}
}
}
```
**Cursor**
Add to Cursor's MCP settings (Settings > MCP Servers):
```json
{
"mcpServers": {
"agentpmt": {
"command": "npx",
"args": ["--package=@agentpmt/mcp-router@latest", "agentpmt-router"],
"env": {
"AGENTPMT_BEARER_TOKEN": "<your-base64-token>"
}
}
}
}
```
**Windsurf, VS Code, Zed, and Other MCP Clients**
Use the same universal configuration block. The only difference is where the config file lives for each client. The structure is always:
```json
{
"command": "npx",
"args": ["--package=@agentpmt/mcp-router@latest", "agentpmt-router"],
"env": {
"AGENTPMT_BEARER_TOKEN": "<your-base64-token>"
}
}
```
#### Step 3: Restart the AI Client
After saving the configuration, restart the AI client. The AgentPMT tools should appear in the tool list within a few seconds.
---
## Method 2: Direct HTTPS Endpoint (For Programmatic Agents)
For agents that support remote MCP servers over HTTP, connect directly without the local connector.
**Endpoint:** `https://api.agentpmt.com/mcp`
**Protocol:** MCP 2.0 (JSON-RPC over streamable HTTP)
**Authentication:** Bearer token in the Authorization header.
```
Authorization: Bearer <base64-encoded-api_key:budget_key>
```
### Example: Initialize Connection
```bash
curl -X POST https://api.agentpmt.com/mcp \
-H "Authorization: Bearer <your-base64-token>" \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "initialize",
"params": {
"protocolVersion": "2025-03-26",
"clientInfo": { "name": "my-agent", "version": "1.0" },
"capabilities": {}
}
}'
```
### Example: List Available Tools
```bash
curl -X POST https://api.agentpmt.com/mcp \
-H "Authorization: Bearer <your-base64-token>" \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/list",
"params": {}
}'
```
### Example: Call a Tool
```bash
curl -X POST https://api.agentpmt.com/mcp \
-H "Authorization: Bearer <your-base64-token>" \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": 3,
"method": "tools/call",
"params": {
"name": "tool-name",
"arguments": { "param1": "value1" }
}
}'
```
### For Clients Supporting Remote MCP URLs
Some clients (Claude Code, OpenAI agents) support remote MCP servers directly:
```json
{
"mcpServers": {
"agentpmt": {
"url": "https://api.agentpmt.com/mcp",
"headers": {
"Authorization": "Bearer <your-base64-token>"
}
}
}
}
```
---
## Verifying the Connection
After setup, verify the connection works:
1. Ask the agent to list its available tools. AgentPMT tools should appear alongside any other configured tools.
2. Look for built-in tools: `AgentPMT-Refresh-Tools` and `AgentPMT-Report-Tool-Issue` confirm the connection is active.
3. Check the AgentPMT dashboard at https://www.agentpmt.com/dashboard -- active connections appear in real time.
## Troubleshooting
**No tools appearing:**
- Verify the Bearer token is correctly base64-encoded (no trailing newlines)
- Confirm the budget has approved products -- tools only appear if the budget has vendors/products enabled
- Restart the AI client after saving config changes
**Authentication errors (401):**
- Regenerate the API key from the dashboard
- Ensure the budget key matches an active budget
- Check that the base64 encoding uses the format `api_key:budget_key` with a colon separator
**Tools listed but calls fail:**
- Check that the budget has sufficient credit balance
- Verify the specific product is approved for the budget
- Review the budget spending cap -- the agent cannot spend beyond the configured limit
## How It Works
The AgentPMT MCP server dynamically assembles a tool catalog based on the budget's permissions. Each tool includes pricing metadata so the agent knows the cost before calling. Budget limits are enforced server-side. Every tool call is logged with a full audit trail visible in the dashboard.
The local STDIO connector (`@agentpmt/mcp-router`) is a thin relay. It does not access local files, does not execute code on the user's machine, and does not cache credentials beyond the current session. All tool execution happens on AgentPMT's cloud infrastructure.
Sessions expire after 2 hours of inactivity and are automatically refreshed on each request during active use.Related Skills
agentpmt-tool-post-on-discord-channel-a58379
Use AgentPMT external API to run the Post On Discord Channel tool with wallet signatures, credits purchase, or credits earned from jobs.
agentpmt-tool-file-management-d789ed
Use AgentPMT external API to run the File Management tool with wallet signatures, credits purchase, or credits earned from jobs.
Add prerequisite install script for Python deps (self-contained skill)
No description provided.
Ark Install
Install Ark on a Kuberenetes Cluster
dev-swarm-install-ai-code-agent
Install AI code agent CLI tools including claude-code, gemini-cli, codex, and github copilot-cli. Use when setting up AI coding assistants or when the user asks to install an AI code agent.
agent-ops-install
Install AgentOps into a new or existing project. Handles .agent/ setup and .github/ merging.
bgo
Automates the complete Blender build-go workflow, from building and packaging your extension/add-on to removing old versions, installing, enabling, and launching Blender for quick testing and iteration.
mcp-create-declarative-agent
Skill converted from mcp-create-declarative-agent.prompt.md
MCP Architecture Expert
Design and implement Model Context Protocol servers for standardized AI-to-data integration with resources, tools, prompts, and security best practices
mathem-shopping
Automatiserar att logga in på Mathem.se, söka och lägga till varor från en lista eller recept, hantera ersättningar enligt policy och reservera leveranstid, men lämnar varukorgen redo för manuell checkout.
math-modeling
本技能应在用户要求"数学建模"、"建模比赛"、"数模论文"、"数学建模竞赛"、"建模分析"、"建模求解"或提及数学建模相关任务时使用。适用于全国大学生数学建模竞赛(CUMCM)、美国大学生数学建模竞赛(MCM/ICM)等各类数学建模比赛。
matchms
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.