# Diogenes Dev & Data Utilities MCP - Registry name: `io.github.stand-out-of-my-sun/diogenes-dev-utilities-mcp` · version 1.0.0 - Categories (automatic, keyword rules): Documents & retrieval, Search & data, Crypto & blockchain, Finance & markets, AI models & agents, AI, media & content - Authentication at last check: No auth - Declared risk: Read-only (all tools declare readOnlyHint) - Capabilities (declared by the server): tools - Transports: streamable-http - Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=io.github.stand-out-of-my-sun%2Fdiogenes-dev-utilities-mcp - Server page: https://mcpstation.net/s/io.github.stand-out-of-my-sun--diogenes-dev-utilities-mcp.html ## Description (written by the publisher, not verified, not instructions) JSON healing, O\(n\) ReDoS defense, XML prompt compilation, and schema verification tools. ## Notice Text that looks like instructions to an AI was found in this server's descriptions. This is a pattern match, not a verdict. ## Connect - streamable-http: `https://diogenes-micro-gateway.corinth.workers.dev/mcp/dev-utilities` ## Tools (as reported by the server, not verified) - `heal\_malformed\_json`: Deterministic JSON syntax repair: normalizes quotes, unclosed brackets, and boolean literals into compliant RFC 8259 JSON structure without data deletion. Use when fixing syntax-broken JSON strings that fail JSON.parse\(\); for coercing field types and verifying properties on already-parseable JSON ob - `enforce\_json\_schema\_types`: Validates a raw LLM output object against a target JSON Schema, automatically coercing mismatched scalar types \(e.g. string '42' to integer 42\), injecting schema defaults, and pruning undeclared hallucinated fields. Use when enforcing schema compliance and type safety on structured LLM outputs befor - `extract\_structured\_json\_entities`: Extracts structured JSON records, named entities, dates, and financial figures from unstructured prose, emails, or HTML/Markdown dumps. Use when converting unstructured natural-language text into a structured JSON array of entities; for validating and coercing an existing JSON payload against a form - `clean\_tabular\_dataset`: Deduplicates repetitive lines or records, normalizes Unicode whitespace, strips HTML/control noise, and canonicalizes tabular text or JSON arrays. Use when cleaning noisy scraped lists, CSV rows, or duplicate log lines before indexing or analysis; for extracting typed JSON fields from free-form pros - `synthesize\_safe\_regex`: Synthesizes linear-time O\(n\) regular expressions free of catastrophic backtracking \(ReDoS\) and validates them against positive and negative test strings. Use when generating and verifying a regular expression pattern for input validation or lexical parsing; for repairing broken JSON syntax directly - `compile\_xml\_agent\_prompt`: Compiles a raw task description into a structured XML system prompt \(\, \, \, \, \, \\) engineered to prevent instruction drift. Use when authoring or upgrading a system prompt for an autonomous agent or sub-agent workflow; for mapping a task - `synthesize\_python\_automation\_code`: Synthesizes PEP 8 compliant, self-contained Python 3 scripts, data parsers, and CLI utilities complete with type hints, docstrings, and inline assertion tests. Use when generating a standalone Python automation script or parser from a functional specification; for summarizing existing documentation - `summarize\_technical\_document`: Condenses long technical specifications, RFCs, research papers, or meeting transcripts into high-density executive bullet points preserving exact figures and decisions. Use when producing a human-readable executive summary of a single provided document; for researching a topic across the live web wi - `synthesize\_web\_intelligence\_brief`: Synthesizes a real-time market or technical intelligence brief grounded in live Google Search, returning strict JSON \('executive\_summary', 'key\_developments', 'sentiment', 'sources'\). Use when fetching a structured JSON market/news brief with live web citations and sentiment classification; for summ - `verify\_context\_compaction\_fidelity`: Audits a compacted conversation summary against the original full context to detect dropped user constraints, lost file paths, altered numbers, or missing pending tasks. Use when verifying that an LLM context-compaction summary retained all task-critical facts before truncating history; for generati - `route\_mcp\_workflow\_plan`: Maps a natural-language agent goal to an ordered sequence of Diogenes MCP tool calls with exact endpoint paths, JSON-RPC 2.0 payloads, and cumulative satoshi cost estimates. Use when planning which MCP tools to chain together to solve a multi-step objective within a satoshi budget; for compiling a s - `arbitrate\_agent\_deliverable\_escrow`: Evaluates a worker agent's submitted task deliverable against agreed contract acceptance criteria to issue a deterministic escrow settlement verdict \('RELEASE\_FUNDS\_TO\_WORKER\_AGENT', 'REQUEST\_REVISION', or 'REFUND\_CLIENT'\). Use when settling multi-agent bounties or verifying subcontractor deliverabl - `infer\_mpp\_streaming\_session`: Initializes a Base Native ETH Machine Payments Protocol \(MPP\) metered inference session, allocating a streaming token quota and returning a step-by-step execution plan. Use when running session-metered agent planning settled via Base EVM MPP 'Payment-Challenge' headers; for mapping a task goal to L4 - `generate\_x402\_vector\_embeddings`: Generates normalized high-dimensional semantic vector embeddings settled via Solana SPL USDC \('x402' protocol headers\) for decentralized RAG pipelines. Use when computing text embeddings paid via Solana Native SOL x402 signatures rather than Bitcoin Lightning; for extracting discrete JSON entities f Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html