Context-Aware AI Chatbot with MCP-Based Action Execution
Bibliographic record
Abstract
Abstract New avenues for intelligent and interactive systems have been made possible by the quick development of artificial intelligence (AI). In order to facilitate intelligent, flexible, and effective human-computer contact, this study presents a Context-Aware AI Chatbot combined with MCP (Multi-Context Processing) based Action Execution. Natural language processing (NLP) is used by the chatbot to comprehend user intent, preserve conversational context, and dynamically carry out real-world actions through MCP integration. This system combines rule-based decision logic, machine learning, and contextual reasoning to bridge the gap between task-oriented automation and static chatbot discussions. Test results show improved user engagement, increased accuracy, and quicker task completion, demonstrating the efficacy of context-aware automation in real-time settings. Keywords: MCP, Task Automation, Artificial Intelligence, Chatbot, NLP, and Context-Aware Systems
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".