{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_pinkpixel-dev-mindbridge-mcp","slug":"pinkpixel-dev-mindbridge-mcp","name":"MindBridge","type":"mcp","url":"https://pinkpixel.dev","page_url":"https://unfragile.ai/pinkpixel-dev-mindbridge-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:pinkpixel-dev/mindbridge-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_0","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.layer.with.unified.interface","name":"multi-provider llm abstraction layer with unified interface","description":"Provides a standardized API that abstracts away provider-specific differences (OpenAI, Anthropic, Ollama, etc.), allowing developers to write model-agnostic code once and switch providers at runtime without refactoring. Implements a provider registry pattern where each LLM backend implements a common interface contract, enabling dynamic provider selection based on task requirements or cost optimization.","intents":["I want to write LLM code that doesn't lock me into one provider","I need to switch between Claude and GPT-4 based on task complexity without rewriting my application","I want to compare model outputs across providers for the same prompt to choose the best one","I need to fall back to a cheaper model if my primary provider is rate-limited or unavailable"],"best_for":["teams building production LLM applications who want provider flexibility","developers prototyping with multiple models before committing to one","cost-conscious builders needing to optimize model selection per request"],"limitations":["Provider-specific features (vision, function calling, streaming parameters) may not be fully abstracted — some capabilities may require provider-specific code paths","Latency overhead from abstraction layer adds ~10-50ms per request depending on provider","Token counting and pricing calculations must be manually configured per provider"],"requires":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Node.js 16+ or Python 3.8+","Network access to provider APIs or local Ollama instance"],"input_types":["text prompts","structured messages with roles (system, user, assistant)","optional parameters (temperature, max_tokens, top_p)"],"output_types":["text completions","structured JSON responses","streaming token streams","usage metadata (tokens, cost)"],"categories":["tool-use-integration","abstraction-layer"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_1","uri":"capability://planning.reasoning.dynamic.provider.selection.and.routing.based.on.task.requirements","name":"dynamic provider selection and routing based on task requirements","description":"Enables intelligent routing of requests to different LLM providers based on configurable criteria such as task type, required capabilities (vision, function-calling, reasoning), cost thresholds, or latency requirements. Uses a routing policy engine that evaluates request metadata against provider capability matrices to select the optimal provider at runtime without manual intervention.","intents":["I want to use GPT-4 for complex reasoning but Claude for summarization to optimize cost","I need to route vision tasks only to providers that support image inputs","I want to automatically fall back to a local Ollama model if cloud APIs are unavailable","I need to enforce a maximum cost per request by selecting cheaper models when possible"],"best_for":["teams running heterogeneous workloads with different model requirements","cost-optimized systems where different tasks have different quality/cost tradeoffs","resilient systems needing automatic fallback strategies"],"limitations":["Routing decisions add latency (typically 5-20ms) for policy evaluation before request dispatch","Capability matrices must be manually maintained as providers update their APIs","No built-in A/B testing framework — comparing provider outputs requires external instrumentation","Routing policies are static configuration; no adaptive learning based on historical performance"],"requires":["Configuration file defining provider capabilities and routing rules","Multiple provider API keys or local model endpoints","Metadata attached to requests (task_type, required_capabilities, max_cost, etc.)"],"input_types":["request with metadata tags (task_type, required_capabilities)","routing policy configuration (YAML or JSON)","provider capability matrix"],"output_types":["selected provider identifier","routing decision rationale (for debugging)","fallback provider chain if primary fails"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_10","uri":"capability://automation.workflow.error.handling.and.automatic.retry.with.exponential.backoff","name":"error handling and automatic retry with exponential backoff","description":"Implements intelligent error handling that distinguishes between retryable errors (rate limits, transient failures) and non-retryable errors (authentication, invalid input). Applies exponential backoff with jitter for retries, and optionally falls back to alternative providers if the primary provider fails, with configurable retry policies per error type.","intents":["I want requests to automatically retry if a provider is temporarily unavailable","I need to handle rate limiting gracefully without failing the entire request","I want to fall back to a backup provider if the primary one fails","I need different retry strategies for different error types (rate limit vs. server error)"],"best_for":["production systems requiring high availability","applications with strict SLA requirements","systems using multiple providers for redundancy"],"limitations":["Retries increase latency; exponential backoff can cause requests to take 30+ seconds in worst case","Fallback to alternative providers may produce different quality outputs; no guarantee of consistency","Retry logic can mask underlying issues; excessive retries may hide systemic problems","Rate limit detection is heuristic-based; some providers' rate limit signals may not be recognized"],"requires":["Retry policy configuration (max retries, backoff strategy, retryable error types)","Fallback provider list","Error classification logic (retryable vs. non-retryable)"],"input_types":["error response from provider","retry policy configuration","fallback provider list"],"output_types":["successful response after retry","error if all retries exhausted","metadata (retry count, providers attempted, final provider used)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_11","uri":"capability://automation.workflow.rate.limiting.and.quota.management.per.provider","name":"rate limiting and quota management per provider","description":"Enforces rate limits and quotas for each provider, tracking request counts and token usage against provider-specific limits. Implements a token bucket or sliding window algorithm to smooth request distribution, with queuing to defer requests that would exceed limits rather than failing them immediately.","intents":["I want to respect each provider's rate limits without exceeding them","I need to queue requests when approaching a provider's quota","I want to distribute requests evenly across multiple providers to avoid hitting limits","I need to track usage against monthly quotas and prevent overage"],"best_for":["high-traffic systems with strict rate limit requirements","cost-conscious applications needing to stay within quotas","systems using multiple providers and needing to balance load"],"limitations":["Queuing adds latency; requests may wait seconds or minutes if quota is exhausted","Rate limit information must be manually configured; no automatic discovery from providers","Distributed systems need shared rate limit state; local tracking doesn't work across multiple servers","Some providers' rate limits are complex (per-minute, per-day, per-token); simplified models may not capture full complexity"],"requires":["Provider rate limit configuration (requests per minute, tokens per day, etc.)","Shared state store for distributed rate limiting (Redis, database, or in-memory for single-server)","Queue implementation for deferred requests"],"input_types":["request with estimated token count","provider rate limit configuration","current usage metrics"],"output_types":["request accepted or queued","estimated wait time if queued","usage metrics (current, remaining quota)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_12","uri":"capability://automation.workflow.batch.processing.and.async.request.handling","name":"batch processing and async request handling","description":"Supports batch processing of multiple requests with optimized throughput, using async/await patterns to handle concurrent requests without blocking. Implements batching strategies like request grouping and token packing to maximize efficiency, with progress tracking and partial failure handling.","intents":["I want to process 1000 documents through an LLM efficiently without sequential calls","I need to group similar requests together to maximize batch efficiency","I want to track progress on a large batch job and handle partial failures gracefully","I need to process requests concurrently while respecting rate limits"],"best_for":["batch processing workflows (document analysis, content generation, etc.)","high-throughput systems processing many requests","offline/asynchronous processing where latency is less critical"],"limitations":["Batch processing adds complexity; error handling for partial failures is non-trivial","Optimal batch size depends on provider and model; no automatic tuning","Progress tracking requires external state store for durability","Concurrent requests multiply API costs and rate limit exposure"],"requires":["Batch job configuration (batch size, concurrency level, timeout)","Request queue or job store","Progress tracking mechanism (database, file system, or in-memory)"],"input_types":["list of requests to process","batch configuration (size, concurrency, timeout)","optional grouping/sorting criteria"],"output_types":["batch job ID for tracking","progress updates (processed count, success/failure counts)","results as they complete","final summary (total processed, success rate, errors)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_2","uri":"capability://tool.use.integration.streaming.response.aggregation.across.multiple.providers","name":"streaming response aggregation across multiple providers","description":"Handles concurrent streaming from multiple LLM providers simultaneously, aggregating token streams in real-time and exposing a unified streaming interface. Implements a multiplexing pattern that buffers and orders tokens from multiple sources, enabling use cases like ensemble voting or competitive streaming where the fastest/best response wins.","intents":["I want to query multiple models in parallel and use the first complete response to minimize latency","I need to aggregate outputs from multiple providers and vote on the best answer","I want to stream responses from the fastest provider while others complete in the background","I need to compare model outputs in real-time as they stream without waiting for all to finish"],"best_for":["latency-sensitive applications where fastest response matters more than cost","ensemble systems combining multiple models for improved accuracy","real-time applications like chatbots needing sub-second time-to-first-token"],"limitations":["Concurrent requests to multiple providers multiply API costs and rate-limit exposure","Token ordering and buffering add complexity; out-of-order delivery can cause garbled output if not handled carefully","Streaming aggregation requires careful resource management to avoid memory bloat with large responses","No built-in deduplication — identical tokens from multiple providers are counted separately"],"requires":["Multiple provider API keys with sufficient rate limits","Support for streaming APIs (not all providers support streaming equally)","Async/await runtime (Node.js or Python asyncio)"],"input_types":["prompt text","list of provider identifiers to query in parallel","aggregation strategy (first-complete, voting, ensemble)"],"output_types":["unified token stream","per-provider token streams (for debugging)","aggregation metadata (which provider won, confidence scores)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_3","uri":"capability://tool.use.integration.mcp.server.integration.for.provider.extensibility","name":"mcp server integration for provider extensibility","description":"Leverages the Model Context Protocol (MCP) to allow new LLM providers to be registered as external MCP servers without modifying the core framework. Each provider implements the MCP interface for model invocation, capability advertisement, and streaming, enabling a plugin architecture where community members can contribute providers as standalone MCP servers.","intents":["I want to add support for a new LLM provider without forking the framework","I need to wrap a proprietary or internal LLM as an MCP server and integrate it with MindBridge","I want to create a custom provider that combines multiple models or adds preprocessing logic","I need to integrate a local fine-tuned model as a provider option"],"best_for":["teams with custom or proprietary LLMs needing integration","framework contributors wanting to add providers without core framework changes","organizations running local models (Ollama, vLLM, etc.) needing MCP wrapping"],"limitations":["MCP server overhead adds ~50-200ms latency per request compared to direct SDK calls","Requires understanding of MCP protocol and server implementation patterns","No automatic provider discovery — servers must be explicitly registered with connection details","Streaming support depends on MCP server implementation quality; poorly implemented servers can cause buffering issues"],"requires":["MCP server implementation (Node.js, Python, or other language with MCP SDK)","MCP server running and accessible (local socket, stdio, or HTTP)","MCP server registration in MindBridge configuration with endpoint details"],"input_types":["MCP server endpoint (socket path, HTTP URL, or stdio process)","provider capability manifest (supported models, features, parameters)","authentication credentials if required"],"output_types":["provider registered and available for routing","capability metadata exposed to routing engine","streaming and non-streaming response handling"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_4","uri":"capability://planning.reasoning.request.level.provider.override.and.a.b.testing","name":"request-level provider override and a/b testing","description":"Allows individual requests to override the default routing policy and explicitly specify which provider(s) to use, enabling per-request A/B testing and experimentation. Supports specifying primary and fallback providers at request time, with built-in instrumentation to track which provider was used and how it performed.","intents":["I want to test a new provider on 10% of requests while keeping the rest on the current provider","I need to force a specific request to use GPT-4 for quality assurance even if the policy would choose a cheaper model","I want to compare two providers' outputs on the same prompt to evaluate quality differences","I need to override routing for a specific user or request type"],"best_for":["teams running A/B tests on model providers","quality assurance workflows requiring specific model selection","gradual migration scenarios where some requests use new providers while others use legacy"],"limitations":["Per-request overrides can bypass cost controls — requires careful authorization to prevent abuse","A/B testing requires external analytics to track outcomes; framework provides no built-in experiment tracking","Override metadata must be passed through entire request pipeline; can complicate middleware and logging","No built-in statistical significance testing — results require external analysis"],"requires":["Request context or headers to specify provider override","Instrumentation to track which provider was used per request","External analytics system to measure A/B test outcomes"],"input_types":["request with optional provider_override field","fallback_providers list","experiment_id or test_group identifier"],"output_types":["response from specified provider","metadata indicating which provider was used","experiment tracking data (test_group, provider, timestamp)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_5","uri":"capability://automation.workflow.cost.tracking.and.budget.enforcement.per.request.and.aggregate","name":"cost tracking and budget enforcement per request and aggregate","description":"Tracks API costs for each request based on token usage and provider pricing, enforcing configurable budgets at request level and aggregate level. Implements a cost calculator that multiplies token counts by provider-specific rates, with hooks to reject requests exceeding budget or trigger alerts when approaching limits.","intents":["I want to prevent requests from using expensive models if they would exceed my daily budget","I need to track how much each request costs and bill it back to a customer or department","I want to get alerts when my monthly LLM spending approaches a threshold","I need to enforce a maximum cost per request to prevent runaway spending on long-context queries"],"best_for":["cost-conscious teams needing budget controls","multi-tenant systems billing customers for LLM usage","organizations with strict spending limits or chargeback requirements"],"limitations":["Cost calculations depend on accurate token counting; some providers' token counts differ from official counts","Pricing data must be manually updated when providers change rates; no automatic price feed integration","Budget enforcement is soft (can reject requests) but doesn't prevent out-of-band API calls outside the framework","No built-in cost optimization recommendations — just tracking and enforcement"],"requires":["Provider pricing configuration (cost per 1K input tokens, cost per 1K output tokens)","Token counting for each provider (can use provider's own token counter or approximation)","Budget configuration (per-request limit, daily limit, monthly limit)"],"input_types":["request with cost_budget field","provider pricing configuration","aggregate budget limits (daily, monthly)"],"output_types":["cost estimate before request","actual cost after request completion","budget remaining (aggregate and per-request)","budget exceeded error if limit violated"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_6","uri":"capability://memory.knowledge.request.context.and.conversation.history.management","name":"request context and conversation history management","description":"Manages conversation history and request context across multiple provider calls, maintaining a unified message format that abstracts away provider-specific message structures. Implements context windowing logic to trim or summarize old messages when approaching token limits, and provides hooks for custom context management strategies.","intents":["I want to maintain a conversation history that works seamlessly across different providers","I need to automatically trim old messages when the conversation gets too long for the model's context window","I want to summarize old messages to preserve context while staying within token limits","I need to inject system prompts or instructions that work consistently across all providers"],"best_for":["multi-turn conversational applications (chatbots, assistants)","systems switching providers mid-conversation and needing consistent context","long-running conversations where context management is critical"],"limitations":["Context windowing strategies (trim vs. summarize) are heuristic-based; no guarantee of optimal context preservation","Summarization requires additional LLM calls, adding latency and cost","Message format abstraction may lose provider-specific features (e.g., vision details in Claude vs. GPT-4)","No built-in persistence — context is in-memory; requires external storage for durability"],"requires":["Message history storage (in-memory or external database)","Context window size for each provider model","Optional summarization model for context compression"],"input_types":["new user message","existing conversation history","context management strategy (trim, summarize, or custom)"],"output_types":["unified message array compatible with all providers","trimmed/summarized context if needed","context metadata (original length, trimmed length, strategy used)"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_7","uri":"capability://tool.use.integration.function.calling.and.tool.use.orchestration.across.providers","name":"function calling and tool use orchestration across providers","description":"Provides a unified function calling interface that abstracts away provider-specific function calling schemas (OpenAI's format vs. Anthropic's vs. others). Implements a schema registry where tools are defined once in a provider-agnostic format, then automatically translated to each provider's function calling format at request time.","intents":["I want to define tools once and use them with any provider without rewriting schemas","I need to handle function calling responses from different providers using a unified interface","I want to validate function arguments before execution regardless of which provider called the function","I need to implement tool use loops that work consistently across all providers"],"best_for":["agentic systems using tool calling across multiple providers","teams building provider-agnostic agents","systems needing consistent tool use behavior regardless of provider"],"limitations":["Schema translation adds latency (~10-30ms) per request with function calling","Some providers have unique function calling features (parallel function calls, streaming) that may not translate cleanly","Tool validation logic must be implemented separately; framework provides no automatic validation","No built-in tool execution — only schema translation and response parsing"],"requires":["Tool definitions in provider-agnostic schema format (JSON Schema or similar)","Provider-specific function calling format mappings","Tool execution handler (external function or API)"],"input_types":["tool definitions with name, description, parameters","request with tools enabled","function calling response from provider"],"output_types":["provider-specific function calling schema","parsed function call with name and arguments","unified function calling response format"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_8","uri":"capability://data.processing.analysis.response.parsing.and.structured.output.extraction","name":"response parsing and structured output extraction","description":"Extracts structured data from LLM responses using configurable parsing strategies (JSON parsing, regex, custom parsers). Implements a parser registry where different output formats can be defined, with automatic validation and error handling for malformed responses, including retry logic with alternative providers.","intents":["I want to extract JSON from model responses and validate it against a schema","I need to parse responses that might be in different formats depending on the provider","I want to automatically retry with a different provider if parsing fails","I need to extract specific fields from unstructured text responses"],"best_for":["systems requiring structured outputs from LLMs","applications parsing responses from multiple providers with different formats","workflows needing robust error handling for malformed LLM outputs"],"limitations":["Parsing failures require retry logic, adding latency and cost","Some providers (like Claude) have better structured output support than others; parsing success rates vary","Custom parsers must be implemented per output format; no universal parser","Validation errors don't always indicate which field is problematic; debugging can be difficult"],"requires":["Output format specification (JSON schema, regex pattern, or custom parser)","Retry strategy if parsing fails","Optional fallback parser for graceful degradation"],"input_types":["raw LLM response text","output format specification","validation schema"],"output_types":["parsed structured data","validation errors if parsing fails","metadata (parser used, retry count, fallback applied)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_pinkpixel-dev-mindbridge-mcp__cap_9","uri":"capability://automation.workflow.request.logging.and.observability.instrumentation","name":"request logging and observability instrumentation","description":"Automatically logs all requests and responses with configurable detail levels, including prompts, responses, provider selection decisions, costs, and latency. Integrates with observability platforms (OpenTelemetry, custom webhooks) to export metrics and traces, enabling debugging and performance monitoring across the entire request lifecycle.","intents":["I want to see which provider was used for each request and why","I need to debug why a request failed or produced unexpected output","I want to monitor latency and cost metrics across all providers","I need to export request logs to an external observability platform for analysis"],"best_for":["production systems needing observability and debugging","teams analyzing provider performance and cost","compliance-heavy applications requiring audit trails"],"limitations":["Logging adds overhead (~5-20ms per request) depending on detail level and export destination","Sensitive data (API keys, user inputs) must be carefully redacted; framework provides no automatic PII detection","Log volume can be large for high-traffic systems; requires external storage and retention policies","Observability platform integration is custom per platform; no universal adapter"],"requires":["Logging configuration (detail level, fields to log)","Optional observability platform endpoint (OpenTelemetry collector, custom webhook)","Storage for logs (file system, database, or external service)"],"input_types":["request with metadata","response from provider","routing decision and cost information"],"output_types":["structured log entry (JSON or custom format)","trace data exported to observability platform","metrics (latency, cost, provider selection)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["API keys for at least one LLM provider (OpenAI, Anthropic, Ollama, etc.)","Node.js 16+ or Python 3.8+","Network access to provider APIs or local Ollama instance","Configuration file defining provider capabilities and routing rules","Multiple provider API keys or local model endpoints","Metadata attached to requests (task_type, required_capabilities, max_cost, etc.)","Retry policy configuration (max retries, backoff strategy, retryable error types)","Fallback provider list","Error classification logic (retryable vs. non-retryable)","Provider rate limit configuration (requests per minute, tokens per day, etc.)"],"failure_modes":["Provider-specific features (vision, function calling, streaming parameters) may not be fully abstracted — some capabilities may require provider-specific code paths","Latency overhead from abstraction layer adds ~10-50ms per request depending on provider","Token counting and pricing calculations must be manually configured per provider","Routing decisions add latency (typically 5-20ms) for policy evaluation before request dispatch","Capability matrices must be manually maintained as providers update their APIs","No built-in A/B testing framework — comparing provider outputs requires external instrumentation","Routing policies are static configuration; no adaptive learning based on historical performance","Retries increase latency; exponential backoff can cause requests to take 30+ seconds in worst case","Fallback to alternative providers may produce different quality outputs; no guarantee of consistency","Retry logic can mask underlying issues; excessive retries may hide systemic problems","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.019795311511906187,"quality":0.6,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"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":"2026-05-24T12:16:27.443Z","last_scraped_at":"2026-05-03T15:18:38.852Z","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=pinkpixel-dev-mindbridge-mcp","compare_url":"https://unfragile.ai/compare?artifact=pinkpixel-dev-mindbridge-mcp"}},"signature":"bMtfq/iTPAkPoSiylrmo4V7+mUwuZv91PvkJcsN3vYD7XT/YWBBsiNB6iJo2PnapeaQyHFsfYkxKOTJgOUUMCw==","signedAt":"2026-06-20T22:55:12.347Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pinkpixel-dev-mindbridge-mcp","artifact":"https://unfragile.ai/pinkpixel-dev-mindbridge-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=pinkpixel-dev-mindbridge-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"}}