🐛 Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement When to use: - When you have error logs, stack traces, or console output to analyze - When debugging complex issues with multiple file involvement - When you need to understand error con...
AI agents call local_debug_context to retrieve information from Ambiance MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool reads and analyzes error logs and codebase files to gather debug context. It extracts information (file paths, line numbers, symbols, error types) without modifying or executing anything. The medium severity reflects that it reads potentially sensitive codebase and error log data, which could expose implementation details or security-relevant information if misused.
From the tool's definition Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement; Parses error logs to extract file paths, line numbers, symbols, and error types
Attacks that exploit this kind of access
🐛 Gather comprehensive debug context from error logs and codebase analysis with focused embedding enhancement When to use: - When you have error logs, stack traces, or console output to analyze - When debugging complex issues with multiple file involvement - When you need to understand error context across the codebase - Before using AI debugging tools to get structured context What this does: - Parses error logs to extract file paths, line numbers, symbols, and error types - Extracts focused error contexts (~200 characters) for precise embedding queries - Uses tree-sitter to build symbol indexes for TypeScript/JavaScript/Python files - Searches codebase for symbol matches with surrounding context - ENHANCED: Uses semantic embeddings with focused error contexts for better relevance - Processes each error/warning separately for improved semantic matching - Ranks matches by relevance (severity, recency, frequency, semantic similarity) - Returns comprehensive debug report ready for AI analysis Input: Error logs or stack traces as text Output: Structured debug context report with ranked matches and semantic insights Performance: Fast local analysis, ~1-3 seconds depending on codebase size Embedding Features: Focused context queries reduce noise and improve relevance. It is categorised as a Read tool in the Ambiance MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Ambiance MCP Server MCP server in PolicyLayer and add a rule for local_debug_context: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Ambiance MCP Server. Nothing to install.
local_debug_context is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the local_debug_context rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for local_debug_context. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
local_debug_context is provided by the Ambiance MCP Server MCP server (sbarron/ambiancemcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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