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"Examples of complex problem-solving using multiple tools" matching MCP servers:

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    Enables high-precision detection, anonymization, encryption and decryption of personally identifiable information (PII) in text using GPT-4o-based detection and advanced cryptographic methods. Supports both deterministic encryption for searchable data and format-preserving encryption for structured identifiers.
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    An MCP server for reading, writing, and analyzing Excel files using Python, pandas, and openpyxl. It enables tasks such as managing multiple worksheets, performing structural data analysis, and creating new files from JSON data.
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    MIT
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    Enables AI agents to interact with local SQLite databases with full CRUD, schema introspection, foreign key relations, generated columns, and multi-format import/export (CSV, JSON, XLSX) through natural language.
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    MIT
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    A governed MCP server enabling LLM agents to query BigQuery, inspect GCS, trigger Airflow DAGs, and run data-quality checks with built-in security guardrails like allow-lists, cost ceilings, and audit trails.
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    MIT
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    An MCP server that provides tools for JSON validation, diffing, and transformation operations such as flattening and renaming. It also enables data format conversion between JSON, CSV, and YAML to streamline data processing for AI agents.
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    MIT
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    MCP server for Grubhub Data Platform operations, providing 40+ tools across 18 service categories for data platform management, observability, analytics, and collaboration.
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    Free remote MCP server for GIS/ArcGIS Online automation — coordinate/EPSG conversion, GeoJSON validation and geometry operations, ArcGIS FeatureServer inspection (feature count/query, schema, health check), and Shapefile/KML/GPX/WKT format conversion. Works with public ArcGIS layers without a token;
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    MIT
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    Enables repair of malformed JSON strings into valid JSON for agent workflows by fixing common LLM formatting issues. Supports cleanup of code fences, smart quotes, unquoted keys, trailing commas, and missing brackets to ensure reliable data parsing.
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    Agent-ready financial intelligence tools for AI agents. Two curated tools — get_stock_snapshot and get_company_metrics — that combine multiple data sources, derive signals (UNDERVALUED, STRONG, ACCELERATING), and pre-compute the math. One call, one agent-friendly response.
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    MIT
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    Enables AI assistants to interact with Databar.ai's data enrichment API, allowing discovery, configuration, and execution of data enrichments across hundreds of providers using natural language.
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    MIT
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    Enables efficient extraction of specific data from JSON APIs using JSONPath patterns, reducing token usage by up to 99% compared to fetching entire responses. Supports single and batch operations for both JSON extraction and raw text retrieval from URLs.
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