B2Bizzio MCP Server
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., "@B2Bizzio MCP Serverget info about B2Bizzio services"
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.
B2Bizzio MCP Server
A TypeScript implementation of a Model Context Protocol (MCP) server for B2Bizzio business intelligence services.
Overview
This MCP server provides a foundation for integrating B2Bizzio services with AI applications that support the Model Context Protocol. It includes sample tools, resources, and prompts that can be extended with real business intelligence functionality.
Related MCP server: Python MCP Server Template
Features
Tools
get_info: Get information about B2Bizzio services
echo: Echo a message back to the user
Resources
welcome: Welcome message and server information
Prompts
business_analysis: Generate comprehensive business analysis prompts
Prerequisites
Node.js 18.0.0 or higher
npm or yarn package manager
Installation
Clone or download this project
Install dependencies:
npm installBuild the TypeScript project:
npm run build
Running the Server
As a Standalone Server
npm startWith MCP Clients
Claude Desktop
Add the server to your Claude Desktop configuration:
Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
{
"mcpServers": {
"b2bizzio-mcp-server": {
"command": "node",
"args": ["path/to/b2bizzio-mcp/build/index.js"]
}
}
}VS Code with MCP Extension
The project includes a .vscode/mcp.json configuration file for development and testing.
Development
Project Structure
b2bizzio-mcp/
├── src/
│ └── index.ts # Main server implementation
├── build/ # Compiled JavaScript output
├── .vscode/
│ └── mcp.json # MCP configuration for VS Code
├── package.json
├── tsconfig.json
└── README.mdAvailable Scripts
npm run build- Compile TypeScript to JavaScriptnpm start- Run the compiled servernpm run dev- Build and run in one command
Extending the Server
Adding New Tools
server.registerTool(
"your_tool_name",
{
description: "Description of your tool",
inputSchema: {
param1: z.string().describe("Parameter description"),
param2: z.number().optional().describe("Optional parameter")
}
},
async ({ param1, param2 }) => {
// Your tool implementation
return {
content: [
{
type: "text",
text: `Result: ${param1}`
}
]
};
}
);Adding New Resources
server.registerResource(
"resource_name",
"your-protocol://resource-uri",
{
description: "Resource description",
mimeType: "text/plain"
},
async () => {
return {
contents: [
{
uri: "your-protocol://resource-uri",
mimeType: "text/plain",
text: "Resource content"
}
]
};
}
);Adding New Prompts
server.prompt(
"prompt_name",
"Prompt description",
{
param1: z.string().describe("Parameter description")
},
async ({ param1 }, extra) => {
return {
description: `Generated prompt for ${param1}`,
messages: [
{
role: "user",
content: {
type: "text",
text: `Your prompt text here with ${param1}`
}
}
]
};
}
);Testing
You can test the server using the MCP Inspector:
npx @modelcontextprotocol/inspector node build/index.jsArchitecture
This server is built on:
@modelcontextprotocol/sdk: Official TypeScript SDK for MCP
zod: Schema validation and type safety
TypeScript: Type-safe development environment
The server uses the stdio transport for communication, making it compatible with most MCP clients.
Security Considerations
The server runs with the permissions of the user account
All tool executions require explicit approval from the MCP client
Input validation is performed using Zod schemas
Error handling prevents information leakage
Contributing
To contribute to this project:
Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Build and test locally
Submit a pull request
License
MIT License - see LICENSE file for details.
Links
Available Tools
2 toolsechoB
Echo a message back to the user
| Name | Required | Description | Default |
|---|---|---|---|
| message | Yes | The message to echo back |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states the basic action without mentioning side effects, output format, safety profile, or any other behavioral traits. 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 a single, tight sentence that conveys the essential meaning without extra words. It is appropriately sized and front-loaded.
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 tool is very simple, and the description sufficiently conveys the basic behavior. However, it could have explicitly stated the return value or lack of side effects for full completeness, especially given the absence of an output schema or annotations.
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 fully describes the 'message' parameter (100% coverage), so the description adds no new semantic information beyond what is already in the schema. The baseline of 3 applies here.
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 ('echo') and resource ('message back to the user'), clearly stating the tool's function. It naturally distinguishes from the sibling tool get_info, which is about retrieving information.
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 guidance is provided on when to use this tool versus alternatives. The description only states the action and gives no context about appropriate use cases, exclusions, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_infoC
Get information about B2Bizzio services
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The topic to get information about |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It implies a read-only operation but does not disclose return format, potential errors, rate limits, or what happens for invalid topics. The single sentence provides minimal transparency beyond the tool's name.
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 sentence with no wasted words, which is concise. However, it is under-specified to the point of providing minimal value, so it is not well-structured for an agent to act on. It is neither verbose nor sufficiently informative.
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 tool is simple (one parameter, no output schema), but the description still fails to explain what kind of information is available, what valid topic values look like, or what response format to expect. Given the lack of annotations and output schema, the description is too sparse to be considered 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?
The schema already provides 100% coverage for the single 'topic' parameter with a basic string description. The tool description adds no additional semantic meaning, so it does not improve on the schema. Baseline 3 is appropriate given the 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 states a clear verb ('Get') and a resource ('B2Bizzio services'), but remains vague about what kind of information is returned or what 'services' encompasses. It does not meaningfully distinguish this tool from the sibling 'echo' beyond being a generic information lookup.
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?
There is no guidance on when to use this tool versus alternatives, no context for what topics are valid, and no mention of any constraints. The description simply restates the tool's basic function without helping an agent decide when to invoke it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
v1.0.0- First observed
echo - First observed
get_info
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: get_info retrieves service information, while echo simply returns a message. There is no overlap or ambiguity between them.
Both names are lowercase verbs, but get_info follows a verb_noun pattern with an underscore, while echo is just a bare verb. The naming is mostly consistent but shows a minor stylistic deviation.
With only two tools, the server feels thin and minimal. The count is borderline acceptable for a very focused utility, but it may be insufficient for a full-featured service.
The domain appears to be B2Bizzio services, but only get_info is relevant to that domain, and echo is an unrelated utility. There are no create, update, delete, or other operational tools, making the surface severely incomplete.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
- ZapierOAuthcom.zapier
Hosted MCP server connecting AI assistants to 9,000+ apps and 40,000+ actions via Zapier.
- UnifAPIOAuthcom.unifapi
Hosted MCP server for live public-data APIs and Skills for AI agents.
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceA basic MCP server template that provides a foundation for building custom tools, resources, and prompts. Serves as a starting point for developers to create their own MCP server functionality.-
- FlicenseNot gradedqualityDmaintenanceA foundational template for building MCP servers in Python using Streamable HTTP transport. Provides example implementations of tools, resources, and prompts to help developers create custom MCP integrations for AI assistants.-
- AlicenseNot gradedqualityDmaintenanceA template/boilerplate MCP server for building custom tools and integrations that enable seamless connections between AI applications and external data sources.152MIT
- FlicenseNot gradedqualityDmaintenanceA minimal MCP server with sample tools, resources, and prompts, plus an aggregator to connect multiple external MCP servers and REST APIs, including experimental OAuth support.-
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/howbizarre/b2bizzio-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server