{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_funkytownk-mcp-analysis","slug":"funkytownk-mcp-analysis","name":"Gold IRA Call Insights","type":"mcp","url":"https://github.com/funkytownk/MCP_Analysis","page_url":"https://unfragile.ai/funkytownk-mcp-analysis","categories":["data-analysis"],"tags":["mcp","model-context-protocol","smithery:funkytownk/mcp_analysis"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_funkytownk-mcp-analysis__cap_0","uri":"capability://data.processing.analysis.transcript.sentiment.analysis","name":"transcript sentiment analysis","description":"This capability analyzes sales call transcripts to determine the overall sentiment expressed by the participants. It employs natural language processing techniques to evaluate word choice, tone, and context, allowing it to classify sentiments as positive, negative, or neutral. By leveraging pre-trained sentiment models, it can provide insights into customer emotions and attitudes during the conversation.","intents":["How can I gauge customer sentiment from my sales calls?","What emotions are present in the call transcripts?","Can I identify negative sentiments that may indicate objections?"],"best_for":["sales teams looking to improve customer interactions"],"limitations":["Accuracy may vary based on the quality of transcripts; requires clear audio-to-text conversion."],"requires":["Python 3.7+","NLTK or spaCy for NLP processing"],"input_types":["text"],"output_types":["structured data"],"categories":["data-processing-analysis","sales-insights"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_funkytownk-mcp-analysis__cap_1","uri":"capability://data.processing.analysis.key.insights.extraction","name":"key insights extraction","description":"This capability extracts key insights from sales call transcripts by identifying important phrases, objections, and compliance risks. It uses a combination of keyword extraction algorithms and machine learning models to highlight significant points in the conversation, enabling sales teams to focus on critical areas for improvement.","intents":["What are the main takeaways from my sales calls?","How can I identify common objections raised by customers?","What compliance risks are present in the transcripts?"],"best_for":["sales managers conducting performance reviews"],"limitations":["May miss nuanced insights without adequate training data; requires domain-specific tuning."],"requires":["Python 3.8+","scikit-learn for machine learning"],"input_types":["text"],"output_types":["structured data"],"categories":["data-processing-analysis","sales-insights"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_funkytownk-mcp-analysis__cap_2","uri":"capability://data.processing.analysis.persuasion.cue.identification","name":"persuasion cue identification","description":"This capability identifies persuasion cues within sales call transcripts by analyzing language patterns and rhetorical techniques used by sales agents. It employs linguistic analysis to detect phrases that indicate attempts to persuade or influence the customer, providing actionable feedback for sales training.","intents":["How can I improve my sales team's persuasive techniques?","What phrases are most effective in persuading customers?","Can I get feedback on my sales pitch effectiveness?"],"best_for":["sales trainers developing coaching materials"],"limitations":["Effectiveness depends on the diversity of training data; may not capture all persuasive techniques."],"requires":["Python 3.9+","TextBlob or similar for linguistic analysis"],"input_types":["text"],"output_types":["structured data"],"categories":["data-processing-analysis","sales-insights"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_funkytownk-mcp-analysis__cap_3","uri":"capability://data.processing.analysis.structured.compliance.risk.assessment","name":"structured compliance risk assessment","description":"This capability assesses compliance risks in sales call transcripts by analyzing the language used against predefined compliance criteria. It utilizes rule-based and machine learning approaches to flag potential compliance issues, ensuring that sales practices adhere to regulatory standards.","intents":["How can I ensure my sales calls comply with regulations?","What compliance risks are present in my sales conversations?","Can I automate compliance checks for sales calls?"],"best_for":["compliance officers overseeing sales practices"],"limitations":["Requires continuous updates to compliance criteria; may not cover all regulatory nuances."],"requires":["Python 3.8+","pandas for data manipulation"],"input_types":["text"],"output_types":["structured data"],"categories":["data-processing-analysis","compliance-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_funkytownk-mcp-analysis__cap_4","uri":"capability://planning.reasoning.actionable.next.steps.recommendation","name":"actionable next steps recommendation","description":"This capability generates recommended next steps based on the analysis of sales call transcripts. By synthesizing insights from sentiment, objections, and compliance risks, it provides tailored action items for sales representatives to follow up on, enhancing the effectiveness of sales strategies.","intents":["What should my sales team do after a call?","Can I get recommendations for follow-up actions?","How can I improve post-call engagement with customers?"],"best_for":["sales teams looking to optimize follow-up processes"],"limitations":["Recommendations may lack context without comprehensive transcript analysis; relies on quality input."],"requires":["Python 3.9+","NLTK for text processing"],"input_types":["text"],"output_types":["structured data"],"categories":["planning-reasoning","sales-insights"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.7+","NLTK or spaCy for NLP processing","Python 3.8+","scikit-learn for machine learning","Python 3.9+","TextBlob or similar for linguistic analysis","pandas for data manipulation","NLTK for text processing"],"failure_modes":["Accuracy may vary based on the quality of transcripts; requires clear audio-to-text conversion.","May miss nuanced insights without adequate training data; requires domain-specific tuning.","Effectiveness depends on the diversity of training data; may not capture all persuasive techniques.","Requires continuous updates to compliance criteria; may not cover all regulatory nuances.","Recommendations may lack context without comprehensive transcript analysis; relies on quality input.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.48999999999999994,"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":"2026-05-24T12:16:26.347Z","last_scraped_at":"2026-05-03T15:19:24.053Z","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=funkytownk-mcp-analysis","compare_url":"https://unfragile.ai/compare?artifact=funkytownk-mcp-analysis"}},"signature":"GUp+1ljslzAIUin6uPgvOFrUiYSo2EPuTDK1sMZmO9SA2dGYyJ+kLrXxTErL+1puwg/l0c7do8uaCsJuujM+Ag==","signedAt":"2026-06-20T02:02:26.387Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/funkytownk-mcp-analysis","artifact":"https://unfragile.ai/funkytownk-mcp-analysis","verify":"https://unfragile.ai/api/v1/verify?slug=funkytownk-mcp-analysis","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"}}