semvec/pypi-packages huggingface dataset:
recursor-mcp 1.0.1
Recursor MCP Server - AI model context protocol integration
Requires Python: >=3.10
Keywords: ai, context, llm, mcp, memory, recursor

# Recursor MCP Server

Model Context Protocol (MCP) server for Recursor, enabling AI agents to search memory, record corrections, and check code safety.

## Features

- **Memory Search**: Find relevant coding patterns and past corrections.
- **Correction Recording**: Learn from user feedback to avoid repeating mistakes.
- **Safety Checks**: Validate code against safety guardrails.
- **Dual Mode**: Supports both stdio (default) and HTTP bridge modes.

## Installation

### Prerequisites

- Python 3.10 or higher
- `recursor` platform installed

```bash
pip install recursor
```

## Usage

### Running the Server (stdio)

The default mode for most MCP clients (like Claude Desktop).

```bash
python3 -m recursor.mcp
```

### Running the HTTP Bridge

Useful for clients that prefer HTTP over stdio.

```bash
python3 -m recursor.mcp --http
```

### Configuration

Set the following environment variables:

- `RECURSOR_API_KEY`: Your Recursor API key.
- `RECURSOR_PROJECT_ID`: The project ID to associate with tool calls.
- `RECURSOR_API_URL`: (Optional) Custom API URL.

## Available Tools

### `search_memory`
Search Recursor's memory for relevant coding patterns, past corrections, or guidelines.

### `add_correction`
Record a correction or improvement to the system's memory based on user feedback.

### `check_safety`
Validate a code snippet against safety guardrails.
Topsis-Ishika-102303460 0.0.1
TOPSIS implementation using Python


# TOPSIS Implementation in Python

**Course:** UCS654 - Predictive Analytics using Statistics  
**Assignment:** Assignment-1 (TOPSIS)  
**Author:** Ishika
**Roll Number:** 102303460  

---

## About the Project

This repository contains a Python implementation of the  
**TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)** method.

TOPSIS is a **multi-criteria decision-making (MCDM)** technique used to rank multiple
alternatives based on their distance from the ideal best and ideal worst solutions.

---

## Installation - USER MANUAL
1. topsis-ishika-102303460 requires Python3 to run.
2. Other dependencies that come installed with this package are :-
    - pandas
    - numpy
3. Package listed on PyPI:- https://pypi.org/project/Topsis-Ishika-102303460/1.0.0/
4. Use the following command to install this package:-
    ```bash
    pip install Topsis-Ishika-102303460==1.0.0

---

## Usage
Run the following command in command prompt:
```bash
topsis <inputFile> <weights> <impacts> <outputFile>

```


Example:
```bash
topsis sample.csv "1,1,1,1" "+,+,-,+" result.csv
```
## Help

To view usage instructions:
```bash
topsis /h
```
## Example

### Input File: `sample.csv`

The input file contains data for different mobile handsets with multiple criteria.

| Model | Storage (GB) | Camera (MP) | Price ($) | Looks (out of 5) |
|------|--------------|-------------|-----------|------------------|
| M1   | 16           | 12          | 250       | 5                |
| M2   | 16           | 8           | 200       | 3                |
| M3   | 32           | 16          | 300       | 4                |
| M4   | 32           | 8           | 275       | 4                |
| M5   | 16           | 16          | 225       | 2                |

## Weights
```csharp
[0.25, 0.25, 0.25, 0.25]
```

## Impacts
```csharp
[+, +, -, +]
```

## Input Command
```bash
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"
```
### Output

| Model | Topsis Score | Rank |
|------|-------------|------|
| M1   | 0.534277    | 3    |
| M2   | 0.308368    | 5    |
| M3   | 0.691632    | 1    |
| M4   | 0.534737    | 2    |
| M5   | 0.401046    | 4    |