{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-mcp-plexus","slug":"mcp-plexus","name":"MCP Plexus","type":"mcp","url":"https://github.com/super-i-tech/mcp_plexus","page_url":"https://unfragile.ai/mcp-plexus","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-mcp-plexus__cap_0","uri":"capability://tool.use.integration.multi.tenant.mcp.server.instantiation.with.isolated.request.contexts","name":"multi-tenant mcp server instantiation with isolated request contexts","description":"Provides a Python framework for spinning up MCP servers that handle multiple independent tenants within a single process, with request-scoped context isolation to prevent cross-tenant data leakage. Each tenant request maintains isolated state through context managers and thread-local or async-context storage, enabling safe multi-tenant deployments without separate server instances.","intents":["Deploy a single MCP server that safely serves multiple customers or organizations without data isolation risks","Build SaaS applications where each customer gets isolated AI tool access through a shared MCP backend","Reduce infrastructure costs by consolidating multiple tenant workloads into one server process"],"best_for":["SaaS platforms integrating MCP tools for multiple end-users","Enterprise teams building internal AI platforms with multi-department access control","Developers optimizing cloud costs by consolidating tenant workloads"],"limitations":["Context isolation relies on proper async/threading patterns — synchronous blocking code can leak context across tenants","No built-in tenant quota enforcement — requires external rate limiting or resource management","Single process failure affects all tenants simultaneously; no per-tenant fault isolation"],"requires":["Python 3.9+","Understanding of async context managers or thread-local storage patterns","MCP client library compatible with the server implementation"],"input_types":["MCP protocol messages (JSON-RPC)","tenant identifiers (headers, JWT claims, or request metadata)"],"output_types":["MCP protocol responses (JSON-RPC)","tenant-isolated tool results"],"categories":["tool-use-integration","multi-tenancy"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_1","uri":"capability://tool.use.integration.oauth.2.1.credential.exchange.and.token.lifecycle.management","name":"oauth 2.1 credential exchange and token lifecycle management","description":"Integrates OAuth 2.1 flows to authenticate users and exchange authorization codes for access tokens, with built-in token refresh, expiration tracking, and secure credential storage. The framework handles the full OAuth handshake (authorization request, callback handling, token exchange) and manages token lifecycle including refresh token rotation and expiration-based re-authentication.","intents":["Authenticate MCP clients against external OAuth providers (Google, GitHub, Okta, custom OIDC)","Securely store and refresh user credentials without exposing secrets in client code","Implement delegated access where MCP tools act on behalf of authenticated users"],"best_for":["Teams integrating MCP with SaaS platforms requiring OAuth authentication","Developers building AI agents that need user-delegated access to external APIs","Enterprise deployments requiring federated identity management"],"limitations":["Requires secure credential storage backend — framework provides abstraction but not built-in encryption at rest","OAuth callback URL must be publicly accessible and HTTPS — not suitable for local-only development without tunneling","Token refresh adds latency (~100-500ms per refresh) if not cached; requires external cache layer for high-throughput scenarios"],"requires":["Python 3.9+","OAuth 2.1 provider credentials (client_id, client_secret)","HTTPS-accessible callback endpoint","Credential storage backend (database, secure vault, or encrypted file system)"],"input_types":["OAuth authorization codes","user credentials (email, password for resource owner password flow)","refresh tokens"],"output_types":["access tokens (JWT or opaque)","refresh tokens","user identity claims"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_2","uri":"capability://tool.use.integration.secure.external.service.integration.via.credential.injected.tool.bindings","name":"secure external service integration via credential-injected tool bindings","description":"Enables MCP tools to call external APIs (REST, GraphQL, RPC) with automatic credential injection from the OAuth token store, using a declarative binding pattern that maps tool definitions to external endpoints. Tools are defined with parameter schemas, and the framework automatically injects authenticated credentials (Bearer tokens, API keys, or custom headers) based on the current tenant and user context.","intents":["Create MCP tools that call external APIs without hardcoding credentials in tool definitions","Build AI agents that can access user-delegated APIs (Gmail, Slack, GitHub) securely","Integrate multiple external services with different authentication schemes into a single MCP server"],"best_for":["Teams building AI agents that need multi-service integration (Slack, GitHub, Jira, etc.)","SaaS platforms exposing customer APIs through MCP tools","Developers implementing least-privilege access where tools only get credentials they need"],"limitations":["Credential injection happens at tool invocation time — no pre-flight validation of token freshness; expired tokens cause tool failures","No built-in retry logic for transient API failures — requires external circuit breaker or retry middleware","Tool parameter validation is schema-based only; no semantic validation of API-specific constraints (e.g., rate limits, quota checks)"],"requires":["Python 3.9+","External service API documentation and authentication scheme","OAuth tokens or API keys for target services","MCP tool schema definition (JSON Schema format)"],"input_types":["tool invocation requests with parameters","authenticated user/tenant context","credential store (OAuth tokens or API keys)"],"output_types":["external API responses (JSON, XML, or raw text)","error responses with credential-related diagnostics"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_3","uri":"capability://automation.workflow.tenant.aware.request.routing.and.context.propagation","name":"tenant-aware request routing and context propagation","description":"Routes incoming MCP requests to tenant-specific handlers and propagates tenant identity through the entire request lifecycle (tool invocation, credential lookup, logging). Tenant context is extracted from request headers, JWT claims, or URL paths and made available to all downstream components via context managers or async context variables, enabling tenant-aware logging, auditing, and resource isolation.","intents":["Ensure all operations (tool calls, credential lookups, logs) are tagged with the correct tenant for audit trails","Implement per-tenant rate limiting or resource quotas without explicit tenant parameter passing","Build multi-tenant dashboards that show tenant-specific activity and errors"],"best_for":["Multi-tenant SaaS platforms requiring audit trails and tenant isolation","Enterprise deployments with compliance requirements (SOC 2, HIPAA) for data segregation","Teams implementing tenant-specific feature flags or behavior customization"],"limitations":["Context propagation relies on proper async/threading discipline — synchronous code or thread pool workers may lose tenant context","No built-in tenant validation — requires external identity provider or token validation service","Context leakage in error handlers or background tasks if not carefully managed; requires explicit context cleanup"],"requires":["Python 3.9+","Tenant identifier in request (header, JWT claim, or URL path)","Async-aware request handling (asyncio or similar)","Logging framework that supports context-aware fields"],"input_types":["MCP protocol messages with tenant identifiers","JWT tokens with tenant claims","HTTP headers with tenant metadata"],"output_types":["tenant-tagged logs and audit events","tenant-isolated tool results","tenant-specific error responses"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_4","uri":"capability://code.generation.editing.declarative.mcp.tool.schema.definition.and.validation","name":"declarative mcp tool schema definition and validation","description":"Provides a Python DSL or decorator-based system for defining MCP tool schemas (input parameters, output types, descriptions) with automatic JSON Schema generation and request/response validation. Tool definitions are declarative (not imperative), enabling the framework to generate OpenAPI/JSON Schema documentation and validate tool invocations against the schema before execution.","intents":["Define MCP tools with clear, validated input/output contracts without manual JSON Schema writing","Generate documentation and type hints for tools automatically from schema definitions","Catch invalid tool invocations early with schema validation before tool code executes"],"best_for":["Teams building large tool libraries where schema consistency matters","Developers wanting IDE autocomplete and type checking for tool parameters","Projects requiring auto-generated API documentation for tool catalogs"],"limitations":["Schema validation adds ~10-50ms per tool invocation; not suitable for ultra-low-latency scenarios","Complex conditional schemas (e.g., 'if type=A then require field X') are difficult to express in JSON Schema; requires custom validators","No runtime type coercion — validation fails if types don't match exactly; requires explicit conversion in tool code"],"requires":["Python 3.9+","JSON Schema or Pydantic models for schema definition","Understanding of MCP tool specification"],"input_types":["Python function signatures or Pydantic models","JSON Schema definitions","tool invocation requests"],"output_types":["JSON Schema documents","validation errors with field-level diagnostics","tool execution results"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_5","uri":"capability://automation.workflow.scalable.async.request.handling.with.connection.pooling","name":"scalable async request handling with connection pooling","description":"Implements async/await-based request handling using Python's asyncio, with connection pooling for external API calls to reduce latency and resource overhead. The framework manages a pool of HTTP connections (via aiohttp or httpx) and reuses them across multiple tool invocations, avoiding the overhead of creating new connections for each external API call.","intents":["Handle hundreds of concurrent MCP client connections without thread-per-connection overhead","Reduce latency for tools that call multiple external APIs by reusing HTTP connections","Scale MCP server to handle high-throughput workloads on modest hardware"],"best_for":["High-throughput SaaS platforms serving many concurrent users","Teams deploying MCP servers on serverless platforms (AWS Lambda, Google Cloud Functions) with limited memory","Developers optimizing for latency-sensitive applications (real-time AI agents)"],"limitations":["Async code requires careful handling of blocking operations — synchronous libraries or CPU-bound code will block the event loop and degrade performance","Connection pool size must be tuned per deployment; too small causes connection exhaustion, too large wastes memory","Debugging async code is harder than synchronous code; stack traces are less readable and require async-aware debugging tools"],"requires":["Python 3.9+","asyncio event loop (built into Python)","Async-compatible HTTP client (aiohttp, httpx, or similar)","Understanding of async/await patterns"],"input_types":["MCP protocol messages","tool invocation requests"],"output_types":["MCP protocol responses","tool results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_6","uri":"capability://safety.moderation.built.in.logging.and.audit.trail.generation.with.tenant.context","name":"built-in logging and audit trail generation with tenant context","description":"Automatically logs all MCP operations (tool invocations, credential lookups, errors) with tenant context, timestamps, and execution metadata, enabling audit trails for compliance and debugging. Logs include tool name, parameters (with sensitive data masked), execution time, and tenant/user identifiers, and can be routed to multiple backends (files, cloud logging services, SIEM systems).","intents":["Generate audit trails for compliance audits (SOC 2, HIPAA, GDPR) showing who accessed what tools when","Debug production issues by correlating logs across multiple tool invocations and tenants","Monitor tool usage patterns and identify anomalies (unusual API calls, high error rates)"],"best_for":["Regulated industries (healthcare, finance) requiring detailed audit trails","Enterprise SaaS platforms with compliance requirements","Teams operating multi-tenant systems where tenant isolation must be auditable"],"limitations":["Logging adds ~5-20ms per tool invocation; high-volume scenarios may require async logging to avoid blocking","Sensitive data masking is heuristic-based (regex patterns) and may miss some sensitive fields; requires custom masking rules","Log volume can grow rapidly in high-throughput scenarios; requires external log aggregation and retention policies"],"requires":["Python 3.9+","Logging backend (file system, cloud logging service, or SIEM)","Log aggregation tool for multi-instance deployments (ELK, Datadog, Splunk, etc.)"],"input_types":["tool invocation requests","execution results and errors","tenant/user context"],"output_types":["structured logs (JSON format)","audit trail events","error reports with context"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_7","uri":"capability://safety.moderation.error.handling.and.recovery.with.credential.aware.diagnostics","name":"error handling and recovery with credential-aware diagnostics","description":"Implements structured error handling that distinguishes between credential-related failures (expired tokens, invalid API keys), transient API errors, and tool logic errors, with automatic recovery strategies. When a tool fails due to an expired token, the framework automatically attempts token refresh before retrying; for transient errors, it implements exponential backoff; for logic errors, it returns detailed diagnostics.","intents":["Automatically recover from transient API failures without user intervention","Distinguish between credential issues and actual API failures to guide debugging","Provide detailed error messages that help users understand why a tool failed"],"best_for":["Resilient AI agents that need to handle API failures gracefully","Multi-tenant systems where credential issues should not crash the server","Teams debugging complex tool failures across multiple external services"],"limitations":["Automatic token refresh can mask underlying credential issues; requires monitoring to detect systemic auth failures","Exponential backoff may delay tool execution for transient failures; not suitable for real-time latency-sensitive applications","Error diagnostics may leak sensitive information (API error messages, token details) if not carefully sanitized"],"requires":["Python 3.9+","Credential store with refresh token support","External service API documentation for error code mapping"],"input_types":["tool invocation requests","API error responses","credential store state"],"output_types":["structured error responses with recovery suggestions","retry metadata (backoff delay, retry count)","diagnostic logs"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_8","uri":"capability://automation.workflow.per.tenant.configuration.and.feature.flag.management","name":"per-tenant configuration and feature flag management","description":"Allows different tenants to have different configurations (enabled tools, rate limits, feature flags) without code changes, using a configuration store (database, config file, or feature flag service). Configuration is loaded per-request based on tenant context and cached to avoid repeated lookups, enabling A/B testing and gradual feature rollouts.","intents":["Enable different tools for different tenants without code deployment","Implement per-tenant rate limits or quotas without code changes","Run A/B tests where some tenants get new features and others get the old behavior"],"best_for":["SaaS platforms with tiered pricing (different features per tier)","Teams running feature experiments or gradual rollouts","Enterprises with per-customer customization requirements"],"limitations":["Configuration caching can lead to stale configs if not refreshed frequently; requires cache invalidation strategy","Configuration changes may not take effect immediately for cached tenants; requires explicit cache flush or TTL-based expiration","No built-in configuration versioning — rolling back a bad config requires manual intervention"],"requires":["Python 3.9+","Configuration store (database, Redis, or feature flag service)","Tenant identifier in request context"],"input_types":["tenant context","configuration keys"],"output_types":["tenant-specific configuration values","feature flag states"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-mcp-plexus__cap_9","uri":"capability://tool.use.integration.extensible.plugin.architecture.for.custom.tool.implementations","name":"extensible plugin architecture for custom tool implementations","description":"Provides a plugin interface that allows developers to register custom tool implementations without modifying the core framework, using a discovery mechanism (module scanning, explicit registration, or entry points) to load plugins at startup. Plugins implement a standard tool interface and are automatically integrated into the MCP server with schema validation and error handling.","intents":["Add custom tools to the MCP server without forking the framework","Build a marketplace of third-party tools that can be installed as plugins","Enable teams to develop tools independently and integrate them into a shared MCP server"],"best_for":["Large organizations with multiple teams building different tools","Open-source projects wanting to accept community contributions","SaaS platforms offering an app marketplace for customer-built integrations"],"limitations":["Plugin isolation is limited — malicious or buggy plugins can crash the entire server or leak data across tenants","No built-in plugin versioning or dependency management — plugins must manage their own dependencies","Plugin discovery and loading adds startup time; not suitable for serverless deployments with strict cold-start budgets"],"requires":["Python 3.9+","Plugin interface documentation","Understanding of Python module loading and entry points"],"input_types":["plugin module paths or entry point names","tool invocation requests"],"output_types":["loaded plugin registry","tool results from plugins"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Understanding of async context managers or thread-local storage patterns","MCP client library compatible with the server implementation","OAuth 2.1 provider credentials (client_id, client_secret)","HTTPS-accessible callback endpoint","Credential storage backend (database, secure vault, or encrypted file system)","External service API documentation and authentication scheme","OAuth tokens or API keys for target services","MCP tool schema definition (JSON Schema format)","Tenant identifier in request (header, JWT claim, or URL path)"],"failure_modes":["Context isolation relies on proper async/threading patterns — synchronous blocking code can leak context across tenants","No built-in tenant quota enforcement — requires external rate limiting or resource management","Single process failure affects all tenants simultaneously; no per-tenant fault isolation","Requires secure credential storage backend — framework provides abstraction but not built-in encryption at rest","OAuth callback URL must be publicly accessible and HTTPS — not suitable for local-only development without tunneling","Token refresh adds latency (~100-500ms per refresh) if not cached; requires external cache layer for high-throughput scenarios","Credential injection happens at tool invocation time — no pre-flight validation of token freshness; expired tokens cause tool failures","No built-in retry logic for transient API failures — requires external circuit breaker or retry middleware","Tool parameter validation is schema-based only; no semantic validation of API-specific constraints (e.g., rate limits, quota checks)","Context propagation relies on proper async/threading discipline — synchronous code or thread pool workers may lose tenant context","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:15.503Z","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=mcp-plexus","compare_url":"https://unfragile.ai/compare?artifact=mcp-plexus"}},"signature":"8ouJKJ1U5SDgETBVwxbOkMhrlYfXPvacnJEINGutAOMkSCZ30zvXKpQGaMVTJo1jUjqD5U/QVNDfDTJAQnnyCQ==","signedAt":"2026-06-21T23:43:34.818Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-plexus","artifact":"https://unfragile.ai/mcp-plexus","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-plexus","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"}}