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Elasticsearch Knowledge Graph for MCP

by j3k0

inspect_files

Analyze and extract relevant content from multiple files using AI, based on specific information needs, keywords, and context. Ideal for precise data retrieval within Elasticsearch Knowledge Graph environments.

Instructions

Agent driven file inspection that uses AI to retrieve relevant content from multiple files.

Input Schema

NameRequiredDescriptionDefault
file_pathsYesPaths to the files (or directories) to inspect
include_linesYesWhether to include the actual line content in the response, which uses more of your limited token quota, but gives more informatiom (default: false)
information_neededYesFull description of what information is needed from the files, including the context of the information needed. Do not be vague, be specific. The AI agent does not have access to your context, only this "information needed" and "reason" fields. That's all it will use to decide that a line is relevant to the information needed. So provide a detailed specific description, listing all the details about what you are looking for.
keywordsYesArray of specific keywords related to the information needed. AI will target files that contain one of these keywords.
reasonNoExplain why this information is needed to help the AI agent give better results. The more context you provide, the better the results will be.

Input Schema (JSON Schema)

{ "$schema": "http://json-schema.org/draft-07/schema#", "additionalProperties": false, "properties": { "file_paths": { "description": "Paths to the files (or directories) to inspect", "items": { "type": "string" }, "type": "array" }, "include_lines": { "description": "Whether to include the actual line content in the response, which uses more of your limited token quota, but gives more informatiom (default: false)", "type": "boolean" }, "information_needed": { "description": "Full description of what information is needed from the files, including the context of the information needed. Do not be vague, be specific. The AI agent does not have access to your context, only this \"information needed\" and \"reason\" fields. That's all it will use to decide that a line is relevant to the information needed. So provide a detailed specific description, listing all the details about what you are looking for.", "type": "string" }, "keywords": { "description": "Array of specific keywords related to the information needed. AI will target files that contain one of these keywords.", "items": { "type": "string" }, "type": "array" }, "reason": { "description": "Explain why this information is needed to help the AI agent give better results. The more context you provide, the better the results will be.", "type": "string" } }, "required": [ "file_paths", "information_needed", "include_lines", "keywords" ], "type": "object" }

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/j3k0/mcp-brain-tools'

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