{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-pearl","slug":"pearl","name":"Pearl","type":"mcp","url":"https://mcp.pearl.com","page_url":"https://unfragile.ai/pearl","categories":["mcp-servers"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-pearl__cap_0","uri":"capability://tool.use.integration.expert.network.discovery.and.matching.via.mcp.protocol","name":"expert network discovery and matching via mcp protocol","description":"Exposes a curated network of 12,000+ certified experts through MCP (Model Context Protocol) server endpoints, enabling AI agents to query and match experts by domain, certification, availability, and expertise tags. The system implements a schema-based expert registry that agents can introspect via MCP's tool discovery mechanism, returning structured expert profiles with credentials, specializations, and contact metadata for downstream agent decision-making.","intents":["Find a certified expert in a specific domain (e.g., 'I need a Kubernetes architect with 10+ years experience') without manual directory searching","Route complex technical problems to the right expert based on agent reasoning about problem requirements","Build multi-step agent workflows that automatically escalate tasks to human experts when needed","Query expert availability and scheduling constraints to coordinate expert engagement"],"best_for":["AI agent builders creating autonomous systems that need human expert escalation","SaaS platforms integrating on-demand expert consultation into their product workflows","Enterprise teams automating expert sourcing for technical problem-solving"],"limitations":["Expert network is curated and certified by Pearl — no ability to add custom experts or private networks","Real-time availability may lag behind actual expert schedules by minutes to hours depending on sync frequency","Matching is based on Pearl's taxonomy and tags — custom domain-specific matching requires pre-mapping to standard categories","No built-in SLA guarantees for expert response time or engagement duration"],"requires":["MCP client implementation (Claude Desktop, custom MCP host, or compatible AI framework)","Pearl API credentials/authentication token","Network connectivity to Pearl's MCP server endpoint","Understanding of MCP protocol and tool schema introspection"],"input_types":["structured queries (domain, expertise tags, availability filters)","natural language problem descriptions (agent-generated)","expert IDs or certification identifiers"],"output_types":["expert profile objects (name, credentials, specializations, contact info)","availability calendars or time slots","matching scores or relevance rankings","structured expert metadata for agent decision trees"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_1","uri":"capability://tool.use.integration.expert.engagement.and.task.delegation.via.mcp.tools","name":"expert engagement and task delegation via mcp tools","description":"Provides MCP tool endpoints for agents to initiate expert engagement, submit detailed problem statements, and track task status. The system handles expert assignment, communication routing, and status updates through Pearl's backend, exposing task lifecycle events (submitted, assigned, in-progress, completed) as structured data that agents can poll or receive via callbacks. Agents can attach context, code snippets, or documentation to tasks for expert review.","intents":["Submit a complex technical problem to an expert and receive structured updates on progress without manual follow-up","Attach code, logs, or documentation to an expert task so they have full context immediately","Track expert task status in agent workflows and trigger downstream actions when tasks complete","Implement multi-step workflows where agent reasoning triggers expert engagement at decision points"],"best_for":["Autonomous agent systems that need to delegate work to humans and wait for results","Hybrid AI-human workflows where agents handle routine tasks and escalate edge cases to experts","Support automation platforms that need to route complex tickets to certified specialists"],"limitations":["Task completion time depends on expert availability and workload — no guaranteed SLA without premium tier","Agents must implement polling or callback handling to track task status; no native streaming updates","Context attachment is limited to text, code, and structured data — binary files or large datasets may require separate storage","No built-in dispute resolution or quality assurance mechanism if agent is unsatisfied with expert response"],"requires":["MCP client with tool-calling capability","Pearl API authentication and account with engagement credits or subscription","Ability to handle asynchronous task completion (polling, callbacks, or event listeners)","Structured problem statement format compatible with Pearl's task schema"],"input_types":["problem description (text or structured format)","code snippets or logs (text)","expert ID or expertise tags for assignment preference","priority level or urgency indicator","deadline or time constraint"],"output_types":["task ID and confirmation","assigned expert profile","task status updates (submitted, assigned, in-progress, completed)","expert response or solution (text, code, or structured data)","task metadata (duration, cost, expert rating)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_2","uri":"capability://tool.use.integration.expert.credential.verification.and.certification.lookup","name":"expert credential verification and certification lookup","description":"Exposes Pearl's expert certification database through MCP tools, allowing agents to verify expert credentials, view certification details, and validate expertise claims before engagement. The system returns structured certification metadata including issuing body, expiration dates, specialization areas, and verification status, enabling agents to make informed decisions about expert suitability for specific technical domains.","intents":["Verify that an expert has valid, current certifications in the required domain before assigning a task","Check expert credential expiration dates to ensure compliance with regulatory or organizational requirements","Compare multiple experts' certifications to select the most qualified candidate for a specialized problem","Audit expert qualifications in agent logs for compliance or quality assurance purposes"],"best_for":["Regulated industries (healthcare, finance, security) where expert credentials must be verified before engagement","Enterprise teams with strict vendor qualification requirements","Agents handling high-stakes problems that require provable expert qualifications"],"limitations":["Certification data is only as current as Pearl's verification refresh cycle — real-time credential validation not supported","Limited to certifications in Pearl's database; custom or niche certifications may not be included","No ability to verify credentials against external issuing bodies in real-time","Certification lookup adds latency to expert selection workflows"],"requires":["MCP client with tool-calling capability","Pearl API authentication","Expert ID or certification identifier to query"],"input_types":["expert ID","certification type or domain filter","date range for expiration validation"],"output_types":["certification objects (name, issuer, expiration date, specialization)","verification status (verified, expired, pending)","credential metadata (issue date, renewal requirements)","structured qualification summary"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_3","uri":"capability://tool.use.integration.expert.availability.and.scheduling.coordination","name":"expert availability and scheduling coordination","description":"Exposes expert calendars and availability windows through MCP tools, enabling agents to check real-time or near-real-time expert availability, reserve time slots, and coordinate scheduling without manual back-and-forth. The system returns availability data (free/busy status, time zones, preferred working hours) and allows agents to propose meeting times or task deadlines that align with expert schedules.","intents":["Check if a specific expert is available before assigning a task to avoid delays","Find the earliest available expert in a domain to minimize task turnaround time","Schedule expert engagement at a specific time that works for both the agent workflow and the expert","Respect expert time zones and working hours when proposing task deadlines"],"best_for":["Time-sensitive agent workflows that need to minimize expert response latency","Global teams coordinating expert engagement across multiple time zones","Agents managing expert capacity and load-balancing across a pool of specialists"],"limitations":["Availability data may be stale if experts don't update calendars in real-time; sync frequency is unknown","No built-in conflict resolution if multiple agents attempt to reserve the same time slot simultaneously","Scheduling is advisory only — no enforcement mechanism if expert becomes unavailable after slot reservation","Time zone handling depends on expert profile accuracy; no automatic time zone conversion validation"],"requires":["MCP client with tool-calling capability","Pearl API authentication","Expert ID or expert pool to query","Ability to handle scheduling conflicts or fallback to alternative experts"],"input_types":["expert ID or list of expert IDs","desired time window or deadline","duration of engagement needed","time zone or location preference"],"output_types":["availability status (available, busy, offline)","free time slots (start time, end time, duration)","expert time zone and working hours","scheduling confirmation or reservation ID"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_4","uri":"capability://tool.use.integration.expert.communication.and.feedback.collection.via.mcp","name":"expert communication and feedback collection via mcp","description":"Provides MCP tools for agents to send messages to assigned experts, receive expert responses, and collect structured feedback or solutions. The system handles message routing, notification delivery, and response tracking, exposing communication history and feedback data as structured records that agents can parse and use for downstream decision-making or learning.","intents":["Send clarifying questions to an expert mid-task without breaking the agent workflow","Receive expert feedback or partial solutions and incorporate them into agent reasoning","Collect structured feedback from experts (ratings, recommendations, lessons learned) for quality assurance","Maintain a communication history for audit trails or agent learning"],"best_for":["Iterative agent workflows that need bidirectional communication with experts","Systems that learn from expert feedback to improve future agent decisions","Compliance-heavy environments that require communication audit trails"],"limitations":["Communication latency depends on expert response time — no guaranteed SLA for message delivery or response","Message format is limited to text and structured data; rich media or file attachments may require separate handling","No built-in encryption or security for sensitive communications — agents must handle data protection","Feedback collection is unstructured unless agents define a specific schema"],"requires":["MCP client with tool-calling capability","Pearl API authentication","Task ID or expert ID to route messages","Ability to handle asynchronous message responses"],"input_types":["message text (question, clarification, update)","structured feedback schema (if collecting specific data)","message priority or urgency flag"],"output_types":["message delivery confirmation","expert response (text or structured data)","feedback objects (ratings, recommendations, notes)","communication history or transcript"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_5","uri":"capability://tool.use.integration.expert.performance.metrics.and.quality.tracking","name":"expert performance metrics and quality tracking","description":"Exposes expert performance data through MCP tools, including task completion rates, average response times, customer satisfaction ratings, and domain-specific quality metrics. The system aggregates historical performance data and allows agents to filter experts by quality thresholds, enabling data-driven expert selection and performance-based routing decisions.","intents":["Select the highest-performing expert in a domain to maximize task quality and minimize rework","Filter out underperforming experts to maintain quality standards in agent workflows","Track expert performance trends over time to identify skill gaps or training needs","Make performance-based routing decisions (e.g., route complex tasks to top-rated experts)"],"best_for":["Quality-critical workflows where expert performance directly impacts business outcomes","Systems that need to maintain SLAs or quality metrics across expert engagements","Teams evaluating expert ROI and optimizing expert pool composition"],"limitations":["Performance metrics are historical and may not reflect current expert capability or market changes","Rating systems may be biased by task difficulty or client expectations rather than pure expert skill","No real-time performance updates — metrics refresh on a schedule determined by Pearl","Limited ability to customize performance metrics for domain-specific quality criteria"],"requires":["MCP client with tool-calling capability","Pearl API authentication","Expert ID or expert pool to query","Sufficient historical data for meaningful performance analysis"],"input_types":["expert ID or list of expert IDs","performance metric type (completion rate, response time, satisfaction rating)","time window for historical analysis","quality threshold or filter criteria"],"output_types":["performance metrics (completion rate, avg response time, satisfaction score)","quality ratings or rankings","performance trends (improvement/decline over time)","expert reliability score or quality index"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_6","uri":"capability://tool.use.integration.multi.expert.collaboration.and.consensus.workflows","name":"multi-expert collaboration and consensus workflows","description":"Enables agents to engage multiple experts simultaneously or sequentially for complex problems, aggregate their responses, and implement consensus or voting mechanisms. The system tracks multiple expert tasks in parallel, collects responses from each expert, and provides tools for agents to compare expert opinions, identify disagreements, and make final decisions based on expert input.","intents":["Get second opinions from multiple experts on a complex technical problem to reduce risk","Implement consensus-based decision-making where agent waits for multiple expert inputs before proceeding","Identify expert disagreements and escalate to a senior expert or human decision-maker","Aggregate expert recommendations into a single structured output for agent reasoning"],"best_for":["High-stakes decision workflows where multiple expert opinions reduce risk","Complex technical problems that benefit from diverse expert perspectives","Compliance-heavy environments that require documented expert consensus"],"limitations":["Coordinating multiple experts increases latency — agent must wait for slowest expert response","No built-in mechanism for resolving expert disagreements; agents must implement custom logic","Cost scales linearly with number of experts engaged; no bulk pricing or efficiency mechanisms","Consensus workflows add complexity to agent state management and error handling"],"requires":["MCP client with tool-calling capability","Pearl API authentication with sufficient credits for multiple expert engagements","Ability to manage multiple concurrent tasks and aggregate responses","Logic for handling expert disagreements or consensus failures"],"input_types":["problem statement (shared across all experts)","list of expert IDs or expertise tags for selection","consensus criteria (majority vote, unanimous, weighted scoring)","timeout for waiting on expert responses"],"output_types":["individual expert responses (structured data)","consensus summary or aggregated recommendation","disagreement analysis (areas of expert disagreement)","confidence score or consensus strength metric"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_7","uri":"capability://tool.use.integration.cost.estimation.and.budget.tracking.for.expert.engagement","name":"cost estimation and budget tracking for expert engagement","description":"Provides MCP tools for agents to estimate expert engagement costs before task submission, track actual costs during execution, and monitor cumulative spending against budgets. The system returns cost breakdowns by expert, task type, and time spent, enabling agents to make cost-aware routing decisions and prevent budget overruns.","intents":["Estimate expert engagement cost before submitting a task to ensure it fits within budget constraints","Compare costs across multiple experts to select the most cost-effective option","Track cumulative spending across multiple expert engagements to stay within budget","Implement cost-aware routing logic that balances quality and expense"],"best_for":["Cost-sensitive workflows where expert engagement must be justified by ROI","Enterprise teams managing expert budgets across multiple projects or departments","Agents that need to make trade-offs between expert quality and cost"],"limitations":["Cost estimates are based on expert rates and historical task durations; actual costs may vary","No built-in cost optimization or negotiation mechanisms — rates are fixed by Pearl","Budget enforcement is advisory only; agents must implement hard limits to prevent overspending","No visibility into cost breakdowns by task component (e.g., research vs. implementation)"],"requires":["MCP client with tool-calling capability","Pearl API authentication with billing information","Budget constraints or cost thresholds defined in agent configuration","Ability to make cost-aware routing decisions"],"input_types":["expert ID or expertise tag","estimated task duration or complexity","budget constraint or maximum cost threshold","cost comparison criteria (lowest cost, best value, fastest)"],"output_types":["cost estimate (total, by expert, by task component)","cost breakdown (hourly rate, estimated hours, total)","budget remaining or overage warning","cost-benefit analysis (cost vs. expected quality or speed)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-pearl__cap_8","uri":"capability://search.retrieval.expert.specialization.and.sub.domain.filtering","name":"expert specialization and sub-domain filtering","description":"Provides fine-grained filtering by expert sub-specializations, experience levels, and domain depth, enabling agents to find experts with specific expertise beyond broad domain categories. Implements hierarchical expertise taxonomy with tags, keywords, and experience metrics, allowing agents to query for experts matching narrow specialization criteria.","intents":["I need an expert in a very specific sub-domain (e.g., 'Kubernetes security' not just 'DevOps')","I want to filter experts by years of experience or seniority level","I need to find experts with specific technology or methodology expertise"],"best_for":["Highly specialized agent workflows requiring deep domain expertise","Technical platforms needing expert filtering by technology stack or methodology","Enterprises with complex expertise requirements across multiple sub-domains"],"limitations":["Specialization taxonomy is predefined by Pearl — no custom specializations","Sub-domain filtering is limited to Pearl's taxonomy depth — very niche specializations may not be available","Experience level is self-reported by experts; no independent verification","Keyword-based filtering may return false positives if taxonomy is incomplete"],"requires":["Pearl API authentication","MCP client with advanced resource filtering","Knowledge of available specialization taxonomy"],"input_types":["domain","sub-specialization tags","experience level range","technology/methodology keywords"],"output_types":["filtered expert list","expertise depth scores","specialization metadata","experience summaries"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude Desktop, custom MCP host, or compatible AI framework)","Pearl API credentials/authentication token","Network connectivity to Pearl's MCP server endpoint","Understanding of MCP protocol and tool schema introspection","MCP client with tool-calling capability","Pearl API authentication and account with engagement credits or subscription","Ability to handle asynchronous task completion (polling, callbacks, or event listeners)","Structured problem statement format compatible with Pearl's task schema","Pearl API authentication","Expert ID or certification identifier to query"],"failure_modes":["Expert network is curated and certified by Pearl — no ability to add custom experts or private networks","Real-time availability may lag behind actual expert schedules by minutes to hours depending on sync frequency","Matching is based on Pearl's taxonomy and tags — custom domain-specific matching requires pre-mapping to standard categories","No built-in SLA guarantees for expert response time or engagement duration","Task completion time depends on expert availability and workload — no guaranteed SLA without premium tier","Agents must implement polling or callback handling to track task status; no native streaming updates","Context attachment is limited to text, code, and structured data — binary files or large datasets may require separate storage","No built-in dispute resolution or quality assurance mechanism if agent is unsatisfied with expert response","Certification data is only as current as Pearl's verification refresh cycle — real-time credential validation not supported","Limited to certifications in Pearl's database; custom or niche certifications may not be included","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"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":"2026-06-17T09:51:03.579Z","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=pearl","compare_url":"https://unfragile.ai/compare?artifact=pearl"}},"signature":"NnmhfUjHgomUahdI7idLH35QoIPkeHku1l2iEnhBr2NsyQpYFjVZKkoKTcbVv1iyJoDDP8+ALrz6uH7C7U7iAw==","signedAt":"2026-06-21T09:56:57.506Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pearl","artifact":"https://unfragile.ai/pearl","verify":"https://unfragile.ai/api/v1/verify?slug=pearl","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"}}