Which integrations are available for this server?

Provides SQL query execution against SQLite databases, supporting both a demo database and file-mode databases.

How do I use MCP Toolkit Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Toolkit Server What's the current weather in San Francisco?" 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.

de en es ja ko ru zh

MCP Toolkit Server

by vyshnavi-nandyala

Overview Schema Source Related Servers Score Discussions Admin

TypeScript

Local

MCP Toolkit Server

Overview

The MCP Toolkit Server is a production-ready Model Context Protocol (MCP) server that equips Claude, ChatGPT, and other LLM agents with a rich set of tools for interacting with databases, external APIs, the file system, and more — directly relevant to the agentic AI wave.

Built with TypeScript and the official @modelcontextprotocol/sdk, this server runs as a local stdio process and integrates seamlessly with Claude Desktop, the MCP Inspector, or any MCP-compatible client.

Related MCP server: MCP Toolkit

Features & Tools

Tool	Description	Example Use Case
`db_query`	Execute SQL queries against SQLite (explore mode with demo DB or file mode)	"Show me all users who placed orders this month"
`api_call`	Make HTTP requests to any REST API with custom headers, params, and body	Fetch data from a weather API, send a webhook
`file_read`	Read file contents from the local filesystem	Read a config file, inspect a log
`file_write`	Write content to files (creates parent dirs automatically)	Save generated code, export data
`file_list`	List files/directories with optional recursive listing and filtering	Explore a project structure
`calculator`	Safely evaluate math expressions (no `eval`)	Calculate compound interest, unit conversions
`get_datetime`	Get current date/time with timezone support	Timestamp logging, scheduling
`json_parser`	Parse, validate, query, and summarize JSON data	Extract fields from API responses
`text_transform`	17+ text operations: case conversion, slug, base64, extract emails/URLs, word count	Data cleaning, text normalization
`get_environment`	Get server environment info (OS, CPU, memory, Node.js version)	Debug, context awareness

Quick Start

Prerequisites

Node.js >= 18.0.0
npm >= 9.0.0

Installation

# Clone the repository
git clone https://github.com/vyshnavi-nandyala/mcp-toolkit-server.git
cd mcp-toolkit-server

# Install dependencies
npm install

# Build the TypeScript project
npm run build

Configure Claude Desktop

Add the server to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "toolkit": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-toolkit-server/dist/index.js"]
    }
  }
}

Replace /absolute/path/to/mcp-toolkit-server with the actual path on your machine.

Restart Claude Desktop, and you'll see a 🔨 icon in the input area — your tools are ready!

Using with MCP Inspector (Debugging)

npx @modelcontextprotocol/inspector node dist/index.js

This opens a web UI where you can manually test each tool, inspect request/response payloads, and debug issues.

Usage Examples

DB Query — Explore the Demo Database

Ask Claude:

"Show me the top 5 products by price from the demo database."

Claude will use the db_query tool:

{
  "sql": "SELECT name, category, price FROM products ORDER BY price DESC LIMIT 5"
}

API Call — Fetch Weather Data

Ask Claude:

"What's the current weather in San Francisco?"

Claude will use the api_call tool:

{
  "url": "https://api.open-meteo.com/v1/forecast?latitude=37.7749&longitude=-122.4194&current_weather=true",
  "method": "GET"
}

File Operations

Ask Claude:

"List all TypeScript files in my project, then read the main entry point."

Claude will chain file_list → file_read:

{ "dirPath": "/path/to/project", "extension": ".ts", "recursive": true }
{ "filePath": "/path/to/project/src/index.ts" }

JSON Parsing

Ask Claude:

"Parse this JSON and extract the first user's email: {\"users\":[{\"email\":\"alice@example.com\"},{\"email\":\"bob@example.com\"}]}"

{
  "json": "{\"users\":[{\"email\":\"alice@example.com\"}]}",
  "operation": "query",
  "path": "users[0].email"
}

Text Transform

Ask Claude:

"Convert this to camelCase and slug: 'My Project Name'"

{ "text": "My Project Name", "operation": "camelcase" }
// → "myProjectName"

{ "text": "My Project Name", "operation": "slug" }
// → "my-project-name"

Architecture

mcp-toolkit-server/
├── src/
│   ├── index.ts                  # Entry point — creates and starts the MCP server
│   ├── tools/
│   │   ├── db-query.ts           # SQLite query tool (explore + file modes)
│   │   ├── api-call.ts           # HTTP request tool (fetch-based)
│   │   ├── file-operations.ts    # file_read, file_write, file_list
│   │   ├── calculator.ts         # Safe math expression evaluator
│   │   ├── datetime.ts           # Date/time with timezone support
│   │   ├── json-parser.ts        # Parse, query, validate, summarize JSON
│   │   ├── text-transform.ts     # 17+ text manipulation operations
│   │   └── environment.ts        # System environment info
│   └── utils/
│       └── helpers.ts            # Shared response-building utilities
├── tests/
│   └── tools.test.ts             # Unit tests (vitest)
├── package.json
├── tsconfig.json
└── README.md

Design Principles

Safety First — SQL injection prevention, no eval(), read-only defaults for DB queries
Modular — Each tool is a self-contained module; easy to add/remove tools
Typed — Full TypeScript with Zod schemas for input validation
Observable — Structured JSON responses with metadata (timing, counts, types)
Developer-Friendly — MCP Inspector support, comprehensive README, unit tests

Adding Custom Tools

Adding a new tool is straightforward:

// src/tools/my-custom-tool.ts
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

export function registerMyCustomTool(server: McpServer): void {
  server.tool(
    "my_custom_tool",
    "Description of what this tool does.",
    {
      param1: z.string().describe("First parameter."),
      param2: z.number().optional().describe("Optional second parameter."),
    },
    async ({ param1, param2 }) => {
      // Your logic here
      return {
        content: [
          { type: "text", text: JSON.stringify({ result: "..." }, null, 2) },
        ],
      };
    }
  );
}

Then register it in src/index.ts:

import { registerMyCustomTool } from "./tools/my-custom-tool.js";
// ...
registerMyCustomTool(this.server);

Development

# Run in development mode (no build step needed)
npm run dev

# Build for production
npm run build

# Run tests
npm test

# Watch tests
npm run test:watch

# Lint
npm run lint

Why This Matters: The Agentic AI Wave

MCP (Model Context Protocol) is the open standard that allows AI agents like Claude to interact with external tools, data sources, and services. Instead of being confined to a chat window, MCP servers give agents the ability to:

Query databases with natural language
Call external APIs to fetch real-time data
Read and write files on the local filesystem
Perform computations and data transformations
Compose multi-step workflows by chaining tools together

This server is a concrete, production-ready implementation of that vision — a toolkit that transforms Claude from a conversational AI into an actionable agent capable of interacting with the real world.

License

MIT License. See LICENSE for details.

Available Tools

10 tools

api_callA

Make an HTTP request to any external API endpoint and return the response.

Supported features:

All HTTP methods: GET, POST, PUT, PATCH, DELETE, HEAD, OPTIONS
Custom headers (including Authorization / Bearer tokens)
JSON and form-urlencoded request bodies
URL query parameters (via url or params)
Configurable timeout (default 15 seconds)
Response includes status code, headers, and body

Use cases:

Fetching data from REST APIs
Sending webhooks
Querying third-party services (weather, maps, etc.)
Testing and debugging API endpoints

ParametersJSON Schema

Name	Required	Description	Default
`url`	Yes	The full URL to send the request to.
`method`	No	HTTP method to use.	GET
`headers`	No	HTTP headers to include (key-value pairs).
`body`	No	Request body. Can be a JSON object (sent as application/json) or a string.
`params`	No	Query parameters to append to the URL (key-value pairs).
`timeout`	No	Request timeout in milliseconds (1,000–60,000).

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses supported methods, custom headers, body types, query parameters, configurable timeout, and response components (status, headers, body). However, it omits details on error handling, redirects, and authentication, which are important for an HTTP tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured: opening sentence states purpose, followed by bulleted features and use cases. Every sentence adds value, with no redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, so the description's mention of 'status code, headers, and body' provides essential but minimal output structure. It covers input features thoroughly. Slightly more detail on output format or error cases would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with detailed descriptions. The description adds overall context (e.g., supported features, body sent as application/json) but does not significantly enhance individual parameter understanding beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Make an HTTP request to any external API endpoint and return the response.' It lists supported HTTP methods, features, and use cases, effectively distinguishing it from sibling tools (e.g., calculator, db_query) which serve different internal functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear use cases (fetching REST APIs, webhooks, third-party services) implying when to use the tool. However, it does not explicitly state when not to use it or compare with alternatives, leaving some ambiguity for edge cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

calculatorA

Evaluate a mathematical expression and return the result.

Supported operations:

Arithmetic: +, -, *, /, %, **
Parentheses for grouping: (2 + 3) * 4
Common functions: abs, ceil, floor, round, sqrt, min, max
Constants: PI, E

Safety:

Does NOT use eval() — uses a safe expression parser
Rejects any non-mathematical input

Examples:

"2 + 3 * 4" → 14
"(2 + 3) * 4" → 20
"sqrt(144)" → 12
"round(3.14159, 2)" → 3.14
"max(10, 20, 30)" → 30

ParametersJSON Schema

Name	Required	Description	Default
`expression`	Yes	The mathematical expression to evaluate (e.g., '2 + 3 * 4').

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses safety (no eval, safe parser), supported operations, and rejection of non-mathematical input. Since no annotations are provided, the description carries full burden and does so well.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose, organized sections for operations, safety, and examples. It is concise without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description is largely complete but does not explicitly state the return type (number). Given the simplicity of the tool, this omission is minor but prevents a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds extensive meaning beyond the schema: it lists supported operations, functions, constants, and provides examples. The schema only describes the parameter as a string, while the description enriches it with context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool evaluates mathematical expressions and returns a result. It is distinct from siblings which handle API calls, database queries, file operations, etc.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates when to use the tool (for math problems) and gives safety guidelines. However, it does not explicitly contrast with siblings or specify when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

db_queryA

Execute a SQL query against a SQLite database and return the results.

Modes:

"explore" (default): Uses a built-in in-memory demo database pre-loaded with sample tables (users, products, orders). Great for quick testing.
"file": Queries a user-specified SQLite file on disk.

Security:

In "explore" mode only SELECT statements are allowed.
In "file" mode only SELECT, EXPLAIN, and WITH ... SELECT are allowed.
DML/DDL (INSERT, UPDATE, DELETE, DROP, etc.) will be rejected.

Returns:

rows: array of objects
rowCount: number of rows returned
columns: list of column names

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes	The SQL SELECT statement to execute.
`mode`	No	Use "explore" for the built-in demo DB, or "file" to query a specific SQLite file.	explore
`dbPath`	No	Path to a .db/.sqlite file (required when mode is 'file').
`limit`	No	Maximum number of rows to return (1–1000).

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so the description carries full burden. Discloses security restrictions, return format, and mode-specific behaviors. Could mention if any side effects occur, but overall transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (modes, security, returns). Every sentence adds value without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensively covers parameters, modes, security, and return format. With no output schema, the description explains the output structure, making it complete for selecting and invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds context about modes and security beyond schema, explaining how 'explore' and 'file' modes behave, which aids parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it executes SQL queries against SQLite databases with two modes ('explore' and 'file'). Distinguishes from siblings like 'api_call' and 'calculator' as a database query tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Specifies when to use each mode and outlines security restrictions (allowed SQL statements per mode). Does not explicitly mention when to avoid using the tool, but the sibling list and context are clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

file_listA

List files and directories at a given path.

Features:

List contents of any directory
Recursive listing with configurable depth
Filter results by file extension
Returns file sizes and types

Use cases:

Exploring project structures
Finding specific file types
Auditing directory contents

ParametersJSON Schema

Name	Required	Description
`dirPath`	Yes	Path to the directory to list (defaults to current directory).
`recursive`	No	Whether to list subdirectories recursively.
`extension`	No	Optional file extension filter (e.g., '.ts', '.json').

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions returning file sizes and types, but does not disclose behavior for edge cases (e.g., permission errors, large directories, missing paths) or whether results are sorted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and well-structured with distinct sections for features and use cases. Every sentence adds value, and the bullet points make it scannable. No unnecessary details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of an output schema, the description covers what the tool does and returns (sizes and types), but does not specify the exact return format (e.g., array of objects). It is complete enough for a simple listing tool but could be more detailed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, so the schema already documents parameters clearly. The description repeats parameter names in features but adds little new semantic meaning beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb (List) and resource (files and directories at a given path). It distinguishes itself from siblings like file_read and file_write by focusing on directory listing, and includes specific features like recursive depth and extension filtering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit use cases (exploring, finding, auditing) which imply when to use the tool. However, it does not mention when not to use it or suggest alternatives, which would strengthen guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

file_readA

Read the contents of a file from the local filesystem.

Features:

Read entire file or a specific byte range
Automatic encoding detection (UTF-8 default)
Returns file metadata (size, last modified)
Supports any text file type

Security:

Rejects paths outside the allowed root directories
Refuses to read binary files or directories

ParametersJSON Schema

Name	Required	Description	Default
`filePath`	Yes	Absolute or relative path to the file.
`encoding`	No	Character encoding (default: utf-8).	utf-8

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description discloses behaviors: reads entire file or byte range (though byte range param missing), automatic encoding detection, returns metadata, and security restrictions. The mention of byte range is inconsistent with the schema, slightly reducing clarity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is well-structured with features and security bullet points. It is front-loaded but includes some redundancy (e.g., headers repeat purpose). Still efficient for the information provided.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description mentions return of metadata (size, last modified). It covers reading behavior and security. Lacks details on error handling or size limits, but is reasonably complete for a simple file read tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions. The description adds context by noting default encoding and the ability to read byte ranges (even if not parameterized), providing value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it reads file contents from the local filesystem. The verb 'Read' and resource 'file' are specific, and it distinguishes itself from sibling tools like file_write and file_list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides features and security constraints, implying when to use (to read a text file). However, it lacks explicit guidance on when not to use or alternatives, though the purpose is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

file_writeA

Write content to a file on the local filesystem.

Features:

Creates parent directories automatically if they don't exist
Overwrites existing files or creates new ones
Supports any text encoding (default: UTF-8)

Use cases:

Saving generated code, configs, or data
Creating log files
Writing reports or exports

ParametersJSON Schema

Name	Required	Description	Default
`filePath`	Yes	Path to the file to write.
`content`	Yes	Content to write to the file.
`encoding`	No	Character encoding (default: utf-8).	utf-8

TDQS

A4.1/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must cover behavior. It discloses automatic parent directory creation, overwrite behavior, and encoding support (default UTF-8). Missing details on permissions or error handling, but adequate for typical usage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with a one-line summary, then Features and Use cases in bullet points. Could be slightly more concise by merging use cases into a sentence, but overall efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple write tool with 3 parameters and no output schema, the description covers purpose, features, and use cases adequately. Missing error handling or edge cases, but not critical for typical scenarios.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description does not add significant meaning beyond the schema's parameter descriptions; it only states 'Supports any text encoding' which repeats the default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Write content to a file on the local filesystem' – a specific verb and resource. This distinguishes it from siblings like file_read and file_list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Lists concrete use cases (saving generated code, configs, log files, reports/exports) but does not explicitly exclude scenarios or compare with alternatives like api_call or db_query.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_datetimeA

Get the current date and time in various formats.

Returns:

ISO 8601 string
Unix timestamp
Individual components (year, month, day, hour, minute, second)
Day of week and week number
Timezone information

This tool does not require any parameters.

ParametersJSON Schema

Name	Required	Description	Default
`timezone`	No	IANA timezone string (e.g., 'America/New_York', 'UTC'). Defaults to the server's local timezone.

TDQS

A3.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description adequately discloses behavioral traits: it lists the return formats and states no parameters are required. However, it does not explain behavior when an invalid timezone is provided or when the timezone parameter is omitted (defaults to server time). The return format details are good.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded with the main purpose. The bullet-style list of return types is easy to scan. One minor inefficiency: the phrase 'This tool does not require any parameters' could be replaced with mentioning the optional parameter, but overall it's well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one optional parameter and no output schema, the description covers the core functionality and return types. However, it misses context about default timezone behavior, error handling, and the optional parameter itself. Given the low complexity, it is minimally adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage with a clear description for the optional 'timezone' parameter. However, the tool description states 'This tool does not require any parameters,' which, while technically true, is misleading by omission because it fails to mention the optional timezone parameter. This adds confusion rather than value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get the current date and time in various formats.' It lists specific return types (ISO 8601, Unix timestamp, components), making the functionality unambiguous. Among siblings, no other tool provides datetime, so differentiation is clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives. While the purpose is obvious (retrieving current datetime), no guidance is given on cases where timezone specification might be needed or that alternatives like get_environment might provide system time. Usage is implied but not articulated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_environmentA

Get information about the current server environment.

Returns:

Operating system details (platform, arch, release)
Node.js version
Server process info (PID, uptime, memory usage)
CPU information
Memory (total, free, used)
Network hostname

This tool does not require any parameters. No sensitive environment variables or secrets are exposed.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses 'No sensitive environment variables or secrets are exposed,' which is important safety information. It also enumerates return categories, aiding understanding of tool behavior. No contradictions or omissions noted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: three sentences that front-load the main purpose, then bullet-like list of returns, then a clarifying note about safety. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple environment inspection tool with no output schema, the description adequately covers the key return categories (OS, Node.js, process, CPU, memory, hostname) and the safety guarantee. No gaps are apparent given the tool's simplicity and lack of parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and schema coverage is 100%. The description adds value by explicitly confirming no parameters are required, which reinforces the schema. Baseline for zero-param tools is 4, and this is met.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get information about the current server environment' and lists specific categories of returned data (OS, Node.js, process, CPU, memory, hostname). It uniquely identifies the tool's purpose and distinguishes it from sibling tools like 'get_datetime' or 'calculator'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly notes 'This tool does not require any parameters,' which is a key usage detail. However, it does not provide explicit when-to-use or when-not-to-use guidance relative to siblings, though the purpose is clear and the context implies it's for environment introspection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

json_parserA

Parse, validate, and query JSON data.

Modes:

"parse": Parse a JSON string and return it formatted.
"query": Extract a specific field using dot-notation (e.g., "data.users[0].name").
"validate": Check if a string is valid JSON and describe its structure.
"summarize": Return a schema-like summary of a JSON object's structure.

Examples:

Parse: '{"a":1,"b":2}' → pretty-printed object
Query: '{"users":[{"name":"Alice"}]}' with path "users[0].name" → "Alice"
Validate: '{"a":1}' → {"valid": true, "type": "object", "keys": ["a"]}

ParametersJSON Schema

Name	Required	Description	Default
`json`	Yes	The JSON string to process.
`operation`	No	The operation to perform on the JSON data.	parse
`path`	No	Dot-notation path for query mode (e.g., "users[0].name").

TDQS

A4.5/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description fully bears the responsibility for behavioral disclosure. It explains the outcomes for each operation (e.g., pretty-printed object for parse, extraction for query, validation result, summary for summarize) and provides examples. It does not cover error handling (e.g., malformed JSON) or performance, which is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear opening sentence followed by a well-structured list of modes and examples. Every sentence provides essential information without redundancy, and the most critical information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has three parameters, no output schema, and no annotations, the description is largely complete. It explains each mode's return, path notation, and provides examples. However, it does not clarify that 'path' is only relevant for query mode (though implied) or describe error behavior for invalid inputs, which prevents a perfect score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage, but the description significantly enriches understanding: it explains the enum 'operation' with four distinct modes, clarifies the dot-notation for 'path', and provides concrete examples demonstrating how parameters interact. The schema descriptions are minimal, so the description adds substantial value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Parse, validate, and query JSON data', immediately stating the tool's resource (JSON data) and action verbs. It clearly distinguishes itself from sibling tools like text_transform, which handle general text transformations, by focusing specifically on JSON operations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists four modes (parse, query, validate, summarize) with specific use cases and examples for each, guiding the agent on when to use each mode. However, it does not explicitly state when not to use this tool or provide alternatives among sibling tools, which would push it to a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

text_transformA

Transform text using various operations.

Supported operations:

"uppercase": Convert to UPPERCASE
"lowercase": Convert to lowercase
"titlecase": Convert to Title Case
"camelcase": Convert to camelCase
"snakecase": Convert to snake_case
"kebabcase": Convert to kebab-case
"reverse": Reverse the text
"trim": Remove leading/trailing whitespace
"slug": URL-safe slug (lowercase, hyphens, no special chars)
"base64_encode": Encode to Base64
"base64_decode": Decode from Base64
"word_count": Count words, characters, sentences, and paragraphs
"remove_duplicates": Remove duplicate lines
"sort_lines": Sort lines alphabetically
"extract_emails": Extract all email addresses from text
"extract_urls": Extract all URLs from text
"hash": Simple hash summary (character frequency)

ParametersJSON Schema

Name	Required	Description	Default
`text`	Yes	The input text to transform.
`operation`	Yes	The transformation operation to apply (see list above).

TDQS

A4.6/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, but the description thoroughly explains each operation's behavior, including edge cases like 'slug' (URL-safe slug) and 'hash' (character frequency). There is no contradiction with missing annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear opening line followed by a bulleted list. While it is somewhat lengthy due to the number of operations, each sentence serves a purpose. It is efficient for the content provided.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool lacks an output schema, but the description implies the return type (transformed text) for most operations. However, for operations like 'word_count' or 'extract_emails', the exact return format is not specified, leaving minor ambiguity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, and the description adds significant value by enumerating the valid operations and briefly explaining each. This goes beyond the schema's generic 'see list above' reference.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool transforms text using various operations, listing 17 specific operations. This is specific, action-oriented, and distinguishes it from sibling tools (none of which are text transformation tools).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists all supported operations, making it clear what can be done. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide any 'when not to use' guidance. Nevertheless, the siblings are unrelated, so the context is sufficient.

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.

10 tool updatesv1.0.0May 10, 2026
- First observedapi_call
- First observedcalculator
- First observeddb_query
- First observedfile_list
- First observedfile_read
- First observedfile_write
- First observedget_datetime
- First observedget_environment
- First observedjson_parser
- First observedtext_transform

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.3/5.0

Scored 2026-05-10 04:05 across 10 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: HTTP requests, math, SQL, file operations, datetime, environment, JSON parsing, text transformation. No overlap or ambiguity.

Naming Consistency5/5

All tool names use snake_case consistently, with a verb_noun pattern for most (file_list, file_read, file_write, get_datetime, get_environment) and simple nouns for others (calculator, json_parser). No mixing of conventions.

Tool Count5/5

10 tools is an appropriate size for a general-purpose utility toolkit, covering diverse common operations without being unwieldy or too sparse.

Completeness4/5

Covers majority of common utility tasks: HTTP, math, SQL, file I/O, datetime, environment, JSON, text transforms. Missing a file delete tool and more advanced date or CSV operations, but overall solid for the toolkit domain.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

hithereiamaliff-mcp-nextcloudOAuth
ai.smithery
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Cloud Storage File Systems Workplace & Productivity
237
Mercado PagoOAuth
com.mercadopago.mcp
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.
Finance E-commerce & Retail App Automation
2
Artificial Wit AIOAuth
com.artificialwit
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
ERP Systems CRM Knowledge & Memory
2
Apideck MCP A
dev.apideck.mcp
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
Developer Tools
42

Related MCP Servers

Calculator MCP Server
Developer Tools Code Execution
QuantGeekDev
A
license
Not graded
quality
D
maintenance
A Model Context Protocol server built with mcp-framework that allows users to create and manage custom tools for processing data, integrating with the Claude Desktop via CLI.
Updated 2 years ago (2025-02-22 23:28 UTC)
9
5
MIT
MCP Toolkit
File Systems Databases Version Control
zxfgds
A
license
Not graded
quality
D
maintenance
A comprehensive Model Context Protocol server implementation that enables AI assistants to interact with file systems, databases, GitHub repositories, web resources, and system tools while maintaining security and control.
Updated 2 years ago (2025-03-04 13:13 UTC)
164
2
MIT
RL-MCP
RAG Systems Agent Orchestration Databases
rlefko
F
license
Not graded
quality
D
maintenance
A Model Context Protocol server that provides AI models with structured access to external data and services, acting as a bridge between AI assistants and applications, databases, and APIs in a standardized, secure way.
Updated a year ago (2025-05-31 18:35 UTC)
2
-
Agent Construct
Agent Orchestration Autonomous Agents Developer Tools
maihengxing999
A
license
Not graded
quality
D
maintenance
Model Context Protocol server that standardizes tool discovery, execution, and context management for AI applications.
Updated a year ago (2025-03-16 16:50 UTC)
MIT