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Uses a schema-based storage model where context entries are indexed by conversation ID, timestamp, and content type, enabling the assistant to maintain coherent state without relying on in-memory buffers or external cloud services. Implements automatic garbage collection and context windowing to prevent unbounded database growth.","intents":["I want my AI assistant to remember previous conversations and context across different tool calls without losing state","I need to build multi-turn agent workflows where each step can access prior decisions and extracted information","I want to debug agent behavior by inspecting the full context history that led to a decision"],"best_for":["teams building multi-step AI agents with stateful workflows","developers integrating multiple MCP tools that need shared context","solo developers prototyping LLM-powered automation without external databases"],"limitations":["SQLite single-writer constraint limits concurrent writes from multiple MCP servers — requires serialization or external coordination","No built-in encryption at rest — sensitive context stored in plaintext in local database file","Context retrieval requires manual query construction — no semantic search or vector similarity built-in","Database file grows unbounded without explicit pruning — requires external maintenance scripts"],"requires":["Node.js 16+","SQLite3 native bindings or better-sqlite3 package","MCP server implementation (Claude Desktop, custom MCP host, or compatible client)"],"input_types":["text (conversation messages, extracted facts, decision logs)","structured JSON (conversation metadata, tool call results, agent state)"],"output_types":["text (retrieved context snippets)","structured JSON (context records with timestamps and metadata)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_1","uri":"capability://tool.use.integration.mcp.tool.registry.and.invocation.orchestration","name":"mcp-tool-registry-and-invocation-orchestration","description":"Exposes a registry of available MCP tools and provides a standardized interface for other MCP tools to discover, invoke, and chain tool calls with automatic context passing. Implements a schema-based tool discovery mechanism where each registered tool declares its input/output types, and the orchestrator validates arguments before invocation, catching type mismatches and missing required parameters. Supports both synchronous and asynchronous tool execution with error handling and result caching.","intents":["I want to build an agent that can discover what tools are available and decide which ones to call based on the task","I need to chain multiple tool calls together where the output of one tool feeds into the next","I want to prevent invalid tool invocations by validating arguments against declared schemas before execution"],"best_for":["developers building multi-tool AI agents with dynamic tool selection","teams implementing tool-use patterns where agents must reason about available capabilities","builders creating MCP-based automation platforms with pluggable tool ecosystems"],"limitations":["Tool registry is in-memory only — does not persist across MCP server restarts without explicit serialization","No built-in timeout or resource limits on tool execution — runaway tools can block the entire MCP server","Schema validation is JSON Schema only — does not support custom validation logic or conditional requirements","Error handling is basic — tool failures propagate as-is without retry logic or fallback mechanisms"],"requires":["Node.js 16+","MCP SDK (mcp package)","Tool implementations that export MCP-compatible schemas"],"input_types":["structured JSON (tool invocation requests with arguments)","tool schema definitions (JSON Schema format)"],"output_types":["structured JSON (tool registry metadata, invocation results)","text (tool execution logs and error messages)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_2","uri":"capability://memory.knowledge.cross.client.context.synchronization","name":"cross-client-context-synchronization","description":"Enables context and memory state to be shared between different MCP clients (e.g., Claude Desktop, custom agents, IDE plugins) by exposing context as queryable MCP resources that any connected client can read and write. Implements a simple versioning scheme where each context update increments a version number, allowing clients to detect stale data and request fresh context. Uses MCP's resource subscription mechanism to push context updates to interested clients in real-time.","intents":["I want my Claude Desktop assistant and my custom agent to share the same conversation context and working memory","I need to sync context between an IDE plugin and a background automation agent so they work on the same task","I want to inspect and modify agent context from multiple clients simultaneously without conflicts"],"best_for":["teams running multiple AI agents that need to coordinate on shared tasks","developers building IDE integrations that need to sync with external agents","organizations with heterogeneous MCP client deployments (Claude Desktop + custom servers)"],"limitations":["No conflict resolution for concurrent writes from multiple clients — last-write-wins semantics can lose data","Version tracking is simple counter-based — does not support branching or merging of divergent context states","Real-time synchronization relies on MCP subscription mechanism — clients must actively subscribe to receive updates","No built-in access control — all connected clients can read and write all context without authentication"],"requires":["Node.js 16+","MCP SDK with resource subscription support","Multiple MCP clients configured to connect to the same devmind-mcp server"],"input_types":["structured JSON (context updates from any client)","MCP resource subscription requests"],"output_types":["structured JSON (synchronized context state)","MCP resource notifications (pushed to subscribed clients)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_3","uri":"capability://memory.knowledge.conversation.history.retrieval.and.filtering","name":"conversation-history-retrieval-and-filtering","description":"Retrieves conversation history from the SQLite context store with support for filtering by conversation ID, time range, message type, and content keywords. Implements pagination to handle large conversation histories without loading entire datasets into memory. Returns results as structured JSON with metadata (timestamps, sender, message type) alongside content, enabling downstream processing and analysis.","intents":["I want to retrieve the last N messages from a conversation to provide context to the AI assistant","I need to find all tool invocations from the past hour to audit what actions the agent took","I want to export a conversation history for analysis or debugging purposes"],"best_for":["developers building chat interfaces that need to load conversation history","teams implementing audit logging for AI agent actions","builders creating analytics dashboards over agent behavior"],"limitations":["No semantic search — filtering is keyword-based only, cannot find conceptually similar messages","Pagination requires manual offset management — no cursor-based pagination for stable iteration","Large conversations (10k+ messages) require multiple queries — no built-in streaming or chunked retrieval","No full-text search index — keyword filtering requires table scans on large datasets"],"requires":["Node.js 16+","SQLite3 with devmind-mcp context store initialized","Conversation ID or time range parameters for filtering"],"input_types":["structured JSON (filter parameters: conversation_id, time_range, message_type, keywords)"],"output_types":["structured JSON (array of conversation messages with metadata)","text (formatted conversation transcript)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_4","uri":"capability://planning.reasoning.agent.decision.and.reasoning.trace.logging","name":"agent-decision-and-reasoning-trace-logging","description":"Captures and stores detailed traces of agent decision-making processes, including intermediate reasoning steps, tool selections, and outcome evaluations. Each trace entry includes the agent's input, reasoning chain, selected action, and result, enabling post-hoc analysis of agent behavior. Implements a hierarchical trace structure where multi-step agent workflows can be represented as nested traces, with each level capturing the reasoning at that abstraction level.","intents":["I want to understand why my agent made a particular decision by reviewing its reasoning trace","I need to debug agent failures by examining the full decision chain that led to an error","I want to evaluate agent performance by analyzing patterns in its decision-making across many traces"],"best_for":["teams building and iterating on AI agents who need visibility into decision-making","developers implementing agent evaluation and monitoring systems","researchers analyzing agent behavior and failure modes"],"limitations":["Trace capture is manual — requires explicit logging calls in agent code, no automatic instrumentation","Nested traces require careful nesting management — no automatic parent-child relationship tracking","Trace storage grows rapidly with complex agents — can consume significant disk space without pruning","No built-in visualization — traces are stored as JSON, requiring external tools for analysis"],"requires":["Node.js 16+","SQLite3 with devmind-mcp context store","Agent code instrumented with trace logging calls"],"input_types":["structured JSON (trace entries with reasoning, actions, and outcomes)"],"output_types":["structured JSON (stored trace records with hierarchical structure)","text (formatted trace logs for human review)"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_5","uri":"capability://memory.knowledge.context.window.management.and.summarization","name":"context-window-management-and-summarization","description":"Automatically manages the size of context windows by summarizing older conversation segments and compressing them into condensed representations. Implements a sliding window approach where recent messages are kept in full detail while older messages are progressively summarized. Uses configurable thresholds to determine when summarization triggers, balancing context freshness with token efficiency.","intents":["I want to keep context windows manageable for long-running agents without losing important information from earlier in the conversation","I need to reduce token usage by summarizing old conversation segments before passing context to the LLM","I want to maintain a compressed history of key decisions and facts while discarding verbose intermediate steps"],"best_for":["teams running long-running agents with extended conversations","developers optimizing token usage and API costs for LLM-based systems","builders implementing memory-efficient multi-turn dialogue systems"],"limitations":["Summarization quality depends on external LLM — requires API calls to Claude, GPT, or similar for each summarization","Summarization is lossy — detailed information is discarded, cannot recover original text after compression","Configurable thresholds require manual tuning — no automatic optimization for different conversation types","Summarization latency adds delay to context retrieval — may impact real-time agent responsiveness"],"requires":["Node.js 16+","SQLite3 with devmind-mcp context store","Access to an LLM API (OpenAI, Anthropic, or compatible) for summarization","Configuration parameters for window size and summarization thresholds"],"input_types":["structured JSON (context window configuration)","text (conversation segments to summarize)"],"output_types":["structured JSON (compressed context with summary metadata)","text (summarized conversation segments)"],"categories":["memory-knowledge","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_6","uri":"capability://memory.knowledge.multi.conversation.isolation.and.namespacing","name":"multi-conversation-isolation-and-namespacing","description":"Supports multiple independent conversations within a single devmind-mcp instance by using conversation IDs as namespace keys. Each conversation maintains its own context, history, and traces, with no cross-contamination between conversations. Implements query filters that automatically scope all context operations to the specified conversation ID, preventing accidental data leakage.","intents":["I want to run multiple independent agent conversations in parallel without their contexts interfering with each other","I need to isolate context for different users or projects within the same MCP server","I want to manage multiple concurrent tasks where each has its own conversation thread"],"best_for":["teams running multi-tenant AI systems where conversations must be isolated","developers building agent platforms that support multiple concurrent users","builders implementing conversation management systems with per-user or per-project isolation"],"limitations":["No built-in access control — conversation isolation is logical only, not enforced at the database level","Conversation IDs must be managed by the client — no automatic generation or validation of conversation ID format","No cross-conversation queries — cannot easily find related conversations or aggregate data across conversations","Conversation cleanup is manual — requires explicit deletion of conversation records, no automatic expiration"],"requires":["Node.js 16+","SQLite3 with devmind-mcp context store","Client-side management of conversation IDs"],"input_types":["structured JSON (conversation ID parameter for all context operations)"],"output_types":["structured JSON (context scoped to specified conversation)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm_npm-devmind-mcp__cap_7","uri":"capability://data.processing.analysis.structured.data.extraction.and.storage","name":"structured-data-extraction-and-storage","description":"Provides a mechanism for agents to extract and store structured data (facts, decisions, extracted entities) from unstructured conversation content. Implements a schema-based storage model where extracted data is validated against declared schemas before storage. Supports querying extracted data by type, enabling agents to retrieve previously extracted facts without re-processing conversation history.","intents":["I want my agent to extract key facts from conversations and store them for later retrieval without re-reading the full history","I need to build a knowledge base from agent conversations by extracting and storing structured entities","I want to validate extracted data against schemas to ensure consistency and correctness"],"best_for":["teams building knowledge extraction pipelines from agent conversations","developers implementing fact-based reasoning systems that need persistent extracted data","builders creating structured data collection systems powered by AI agents"],"limitations":["Schema validation is JSON Schema only — does not support complex custom validation logic","Extraction is manual — requires explicit extraction calls in agent code, no automatic entity recognition","No deduplication — duplicate facts can be stored if extraction logic doesn't check for existing records","No versioning — updates to extracted data overwrite previous versions without history"],"requires":["Node.js 16+","SQLite3 with devmind-mcp context store","JSON Schema definitions for extracted data types"],"input_types":["structured JSON (extracted data conforming to declared schemas)"],"output_types":["structured JSON (stored extracted data records)","text (formatted extracted data for human review)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","SQLite3 native bindings or better-sqlite3 package","MCP server implementation (Claude Desktop, custom MCP host, or compatible client)","MCP SDK (mcp package)","Tool implementations that export MCP-compatible schemas","MCP SDK with resource subscription support","Multiple MCP clients configured to connect to the same devmind-mcp server","SQLite3 with devmind-mcp context store initialized","Conversation ID or time range parameters for filtering","SQLite3 with devmind-mcp context store"],"failure_modes":["SQLite single-writer constraint limits concurrent writes from multiple MCP servers — requires serialization or external coordination","No built-in encryption at rest — sensitive context stored in plaintext in local database file","Context retrieval requires manual query construction — no semantic search or vector similarity built-in","Database file grows unbounded without explicit pruning — requires external maintenance scripts","Tool registry is in-memory only — does not persist across MCP server restarts without explicit serialization","No built-in timeout or resource limits on tool execution — runaway tools can block the entire MCP server","Schema validation is JSON Schema only — does not support custom validation logic or conditional requirements","Error handling is basic — tool failures propagate as-is without retry logic or fallback mechanisms","No conflict resolution for concurrent writes from multiple clients — last-write-wins semantics can lose data","Version tracking is simple counter-based — does not support branching or merging of divergent context states","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.903Z","last_scraped_at":"2026-05-03T14:23:59.757Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=npm-devmind-mcp","compare_url":"https://unfragile.ai/compare?artifact=npm-devmind-mcp"}},"signature":"Y+H5CMXt8cvzZpXkMC9QogaH9XmhpZq+Tq4on5S7rjsQ5jdU8OWbyUFhMxygAfc1w1/1cUIhurcupcg9XpxhCA==","signedAt":"2026-06-19T21:01:46.893Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/npm-devmind-mcp","artifact":"https://unfragile.ai/npm-devmind-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=npm-devmind-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}