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π§ Add Local Intelligence MCP - Swift-based text processing server #325
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name: local_intelligence_mcp | ||
image: mcp/local_intelligence_mcp | ||
type: server | ||
meta: | ||
category: ai | ||
tags: | ||
- ai | ||
about: | ||
title: Local_intelligence_mcp | ||
description: "A Swift-based, cross-platform Model Context Protocol (MCP) server that extends Appleβs on-device Intelligence features to local AI agents in a secure and privacy-preserving way.\n\nLocal Intelligence MCP provides text processing, summarization, and contextual analysis tools designed to augment Apple Intelligence capabilities within your own applications " | ||
icon: https://avatars.githubusercontent.com/u/1335392?v=4 | ||
source: | ||
project: https://github.com/bretbouchard/Local_Intelligence_MCP |
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[ | ||
{ | ||
"name": "text_normalize", | ||
"description": "Clean and standardize text input by removing extra whitespace, normalizing punctuation, and applying consistent formatting rules", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to normalize and clean" | ||
}, | ||
{ | ||
"name": "removeFillers", | ||
"type": "boolean", | ||
"desc": "Whether to remove filler words and unnecessary content" | ||
}, | ||
{ | ||
"name": "standardizeFormat", | ||
"type": "boolean", | ||
"desc": "Whether to apply standard formatting rules" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "text_chunking", | ||
"description": "Split large text documents into manageable chunks while preserving context and meaning boundaries", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to split into chunks" | ||
}, | ||
{ | ||
"name": "maxChunkSize", | ||
"type": "number", | ||
"desc": "Maximum size of each chunk in characters" | ||
}, | ||
{ | ||
"name": "overlap", | ||
"type": "number", | ||
"desc": "Number of characters to overlap between chunks" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "summarization", | ||
"description": "Generate comprehensive summaries of text content with configurable style and length parameters", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to summarize" | ||
}, | ||
{ | ||
"name": "style", | ||
"type": "string", | ||
"desc": "Summary style: executive, technical, brief, detailed" | ||
}, | ||
{ | ||
"name": "maxLength", | ||
"type": "number", | ||
"desc": "Maximum length of the summary in words" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "pii_redaction", | ||
"description": "Detect and redact personally identifiable information while preserving technical and domain-specific terminology", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to scan for PII" | ||
}, | ||
{ | ||
"name": "policy", | ||
"type": "string", | ||
"desc": "Redaction policy: conservative, balanced, aggressive" | ||
}, | ||
{ | ||
"name": "preserveAudioTerms", | ||
"type": "boolean", | ||
"desc": "Whether to preserve audio and technical terminology" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "focused_summarization", | ||
"description": "Create targeted summaries focusing on specific aspects or topics within the content", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to analyze and summarize" | ||
}, | ||
{ | ||
"name": "focus", | ||
"type": "array", | ||
"desc": "Array of focus areas: key_decisions, action_items, technical_details, etc." | ||
}, | ||
{ | ||
"name": "analysisDepth", | ||
"type": "string", | ||
"desc": "Analysis depth: surface, standard, deep" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "content_purpose_detector", | ||
"description": "Analyze and identify the purpose, intent, and context of text content with comprehensive categorization", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to analyze for purpose and intent" | ||
}, | ||
{ | ||
"name": "context", | ||
"type": "object", | ||
"desc": "Additional context information including domain and analysis preferences" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "tag_generation", | ||
"description": "Extract relevant keywords, topics, and metadata tags from text content with confidence scoring", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to analyze for tag generation" | ||
}, | ||
{ | ||
"name": "maxTags", | ||
"type": "number", | ||
"desc": "Maximum number of tags to generate" | ||
}, | ||
{ | ||
"name": "includeTechnical", | ||
"type": "boolean", | ||
"desc": "Whether to include technical terminology in tags" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "schema_extraction", | ||
"description": "Extract and create structured data schemas from unstructured text content", | ||
"arguments": [ | ||
{ | ||
"name": "text", | ||
"type": "string", | ||
"desc": "The text content to extract structured data from" | ||
}, | ||
{ | ||
"name": "schemaType", | ||
"type": "string", | ||
"desc": "Type of schema to extract: person, event, product, technical_spec" | ||
}, | ||
{ | ||
"name": "outputFormat", | ||
"type": "string", | ||
"desc": "Output format: json, yaml, xml" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "health_check", | ||
"description": "Perform comprehensive health monitoring and system diagnostics with detailed status reporting", | ||
"arguments": [] | ||
}, | ||
{ | ||
"name": "system_info", | ||
"description": "Retrieve detailed system information including device capabilities, performance metrics, and configuration details", | ||
"arguments": [ | ||
{ | ||
"name": "categories", | ||
"type": "array", | ||
"desc": "Information categories to retrieve: device, performance, network, capabilities" | ||
}, | ||
{ | ||
"name": "includeSensitive", | ||
"type": "boolean", | ||
"desc": "Whether to include sensitive system information" | ||
} | ||
] | ||
}, | ||
{ | ||
"name": "capabilities_list", | ||
"description": "List all available tools, capabilities, and system features with detailed descriptions and usage information", | ||
"arguments": [ | ||
{ | ||
"name": "category", | ||
"type": "string", | ||
"desc": "Filter by capability category: text_processing, analysis, system, extraction" | ||
}, | ||
{ | ||
"name": "includeExamples", | ||
"type": "boolean", | ||
"desc": "Whether to include usage examples for each capability" | ||
} | ||
] | ||
} | ||
] |
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The description text is truncated mid-sentence, ending with 'within your own applications ' without completing the thought or closing properly.
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