{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Correct completed actions, invalid calls, retries, latency, and cost per completed task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2609.24620",
  "title": "Ascent: An Agentic System over the Model Context Protocol for Real-World Clinical Data Analysis",
  "summary": "The method replaces a fixed, hand-coded analysis pipeline for epidemiological question answering with an LLM agent that calls medical coding, SQL, and cohort-analysis tools exposed through a shared Model Context Protocol surface. It costs more tool calls and longer runtimes per query, depends on capable (likely API-based) models, and adds orchestration and schema-integration complexity rather than GPU memory. It can fail through unrecognized pharmacoepidemiological errors in population, denominator, and time-window choices, and the reported gains may not transfer outside clinical data domains or to weaker models.",
  "rationale": "The accuracy gains are substantial but demonstrated only on clinical data tasks with capable models, so a constrained team should validate it on their own workloads before adopting it in production.",
  "technique": "Agentic clinical data analysis over MCP tools",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24620",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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