"How to create tasks or issues in Jira" matching MCP servers:

Enables AI agents to convert CSV text into JSON arrays with automatic delimiter detection and optional header-row handling, accessed through a pay-per-call x402 micropayment API.

Convert PDFs to structured JSON. Extract invoices, bank statements, contracts, and more. Pay per call via x402 USDC.

The Excel to JSON MCP provides a standardized interface for converting Excel and CSV data into JSON format using the Model Context Protocol.

The JSON to Excel MCP provides a standardized interface for converting (1)JSON data (2)URL pointing to JSON files into CSV format

Converts JSON data to TOON (Token-Oriented Object Notation) format and back, reducing token usage by 30-60% for more efficient LLM applications.

Enables AI agents to rank US healthcare organizations for clinical trial site feasibility, predicting patient accrual for a Graves' disease protocol using claims and registry data.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

MCP server for configuring the konsol Frappe app via the CLI. Enables AI clients to manage dimensions, measures, fact tables, connectors, and schemas using natural language.

An open-source MCP server that imports HR CSV data into an in-memory SQLite database for structured querying and metadata retrieval. It enables users to perform read-only SQL queries and structured searches on employee data through natural language.

Enables Claude to list Iceberg tables and run read-only DuckDB SQL queries, including time travel by snapshot, with per-caller token authentication and audit logging.

Converts eFax documents (PDF, TIFF, CCD XML) from OpenText Fax Server Software into structured JSON format with OCR support, metadata extraction, and batch processing capabilities.

An implementation that integrates with Odoo ERP systems, enabling AI assistants to interact with Odoo data and functionality through the Model Context Protocol.

A lightweight, powerful, and flexible workflow engine that executes tasks defined in YAML configuration files.

Enables AI clients to perform opponent scouting, retrieve athlete box scores, diagnose P1–P6 rotation sideout issues, audit serve-receive, and export event-level touches to DataVolley (.dvw) or pandas.

Enables LLMs to profile and analyze tabular data files (CSV, Parquet, Excel, JSON) by extracting schema, statistics, data quality issues, and dtype suggestions, returning structured JSON.

Extracts structured JSON data from unstructured text using predefined schemas for receipts, invoices, resumes, and emails. It allows users to transform messy text into organized data through built-in or custom-defined fields.

Enables coding agents to interact with Metaflow workflows, including querying flows, runs, tasks, logs, and artifacts across any Metaflow backend.

Enables AI agents to work with a real Excel-compatible spreadsheet engine, allowing them to create and manipulate structured data blocks, write and evaluate formulas, and save genuine .xlsx files with live formulas for human verification and reuse.

Enables AI assistants to search multiple tech job boards at once, summarize what the market is asking for, and rank postings by how well they match a user's own skills.

Enables accountants and bookkeepers to ask practice-wide questions across every connected client Xero organisation in one read-only view — overdue invoices, payroll shortfalls, client health scores, trending-trouble lists and practice totals such as revenue and average DSO. It also generates ready-to-send Excel month-end packs per client, with every reported figure accompanied by the rule or formula that produced it and no financial data persisted to disk.