Heor Agent

HEORAgent MCP Server

AI-powered Health Economics and Outcomes Research (HEOR) agent as a Model Context Protocol server.

Try it now → HEORAgent on ChatGPT (ChatGPT Plus / Team)

· Web UI (Claude, BYOK)

· npx heor-agent-mcp for Claude Desktop / Claude Code

Automates literature review across 44 data sources, risk of bias assessment (RoB 2 / ROBINS-I / AMSTAR-2), EQ-5D value set impact estimation, state-of-the-art cost-effectiveness modelling, HTA dossier preparation for NICE / EMA / FDA / IQWiG / HAS / EU JCA, and a persistent project knowledge base — all callable as MCP tools from Claude.ai, Claude Code, and any MCP-compatible host.

Built for pharmaceutical, biotech, CRO, and medical affairs teams who need rigorous, auditable HEOR workflows without building infrastructure from scratch.

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First 60 seconds

Verify your install works before wiring it into Claude / Cursor / Continue. Open two terminal tabs:

Tab 1 — start the server in HTTP mode:

MCP_HTTP_PORT=8080 npx heor-agent-mcp@latest

You should see:

HEORAgent MCP server running on HTTP port 8080

Tab 2 — confirm it responds:

curl -s http://localhost:8080/health

Expected output:

{"status":"ok","server":"heor-agent-mcp","version":"1.10.2"}

✅ If you see the JSON above, the npm package works on your machine. Any further issues are in your MCP client config (Claude Desktop / Cursor / Continue), not the server.

❌ If you see command not found, run node --version — you need Node ≥20. If you see a different error, file a quick issue at https://github.com/neptun2000/heor-agent-mcp/issues with the output.

Now stop Tab 1 (Ctrl+C) and pick your client below — you don't need the HTTP mode for the actual integration; Claude / Cursor / Continue all use stdio.

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Quick Start (per client)

Pick your MCP host:

Claude Code

claude mcp add heor-agent -- npx heor-agent-mcp

Then restart Claude Code.

Claude Desktop / claude.ai Desktop

Edit your MCP config file (~/Library/Application Support/Claude/claude_desktop_config.json on macOS) and add:

{

"mcpServers": { "heor-agent": { "command": "npx", "args": ["heor-agent-mcp"] } } }

Then restart Claude Desktop.

Cursor / Continue / Cline

Same config shape as Claude Desktop above; the file path differs by client:

• Cursor: Settings → MCP → Add new MCP server
• Continue: ~/.continue/config.json under the mcpServers key
• Cline: Settings → MCP Servers → Edit MCP Settings

Hosted (no install)

• ChatGPT (Plus / Team): HEORAgent on ChatGPT — type /heor to use it; works on any conversation.
• Web UI (Claude, BYOK): web-michael-ns-projects.vercel.app — bring your Anthropic API key; runs the full v1.6.3 toolset.

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Your first prompt

Once your MCP host is configured, paste any of these to verify end-to-end:

Run a literature search for semaglutide cost-effectiveness in T2D

using PubMed, NICE TAs, and ICER reports. Set runs=2.

Run irb_review for an industry-funded interventional Phase 2 trial in relapsed MM — multi-site US+EU, pseudonymized data, greater-than-minimal risk. I need the review tier, GDPR/HIPAA DMP, SAE framework, and the ready-to-paste cover letter.

Run jca_pico_scope for osimertinib in EGFR-mutant 2L NSCLC across DE/FR/IT/ES/NL. Then prepare an EU JCA dossier draft using the picos.

The first prompt exercises literature_search + validate_links (free, no API keys needed). The second exercises irb_review (pure decision tree, instant). The third exercises jca_pico_scope → hta_dossier pipeline.

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What's new

See CHANGELOG.md for full version history. Current: v1.23.0 (45 tools, 44 data sources).

v1.17.0–v1.23.0 — Living Evidence Intelligence (review → reimbursement)

A connected RWE + cross-deliverable layer (see docs/FEATURES.md for the full table):

• RWE & real-world safety: rwe.method_select (study-design selection), pv.comparative_safety (class-level FAERS-style AE ranking), evidence.triangulation (per-outcome RCT↔RWE concordance).
• Governed social listening: rwe.social_listening_protocol + pv.social_listening_triage (GVP Module VI ICSR triage — no scraping).
• One source of truth: evidence.claim_registry (author/auto-import a figure once), evidence.consistency_check (detect drift across dossier/publication/payer), publication.draft (reuse claims; CONSORT/STROBE/PRISMA/CHEERS + GPP2022/ICMJE).
• Living orchestration: evidence.gap_analysis (iEGP), workflow.living_evidence (SLR → living KB → JCA/HTA runbook), hta.living_gvd (regenerate only the GVD sections whose figures changed).

v1.13.0 — AI Transparency Disclosure (ISPOR ELEVATE-GenAI aligned)

16 tools now accept an ai_disclosure_level parameter:

Default by tool tier: HTA/regulatory tools (hta_dossier, hta_workflow, jca_pico_scope, pv_classify, etc.) default to "submission"; analysis tools (risk_of_bias, cost_effectiveness_model, etc.) default to "standard". Pass ai_disclosure_level: "off" to suppress.

Environment-level default: set HEORAGENT_DISCLOSURE_LEVEL=off|standard|submission to override the built-in per-tool defaults globally.

Web UI persona defaults: payer and HTA-reviewer personas always use "submission"; analyst personas default to "standard" and switch to "off" for scratch / exploratory prompts.

v1.0.4 highlights (still in v1.6.3)

Pharmacovigilance + workflow orchestration:

• `pv_classify` tool — classifies a planned study into its EMA pharmacovigilance regulatory category (PASS imposed/voluntary, PAES, RMP Annex 4, DUS, active surveillance registry, pregnancy registry, spontaneous reporting, ICH E2E plan). Returns the matching GVP module (V/VI/VIII/VIII Addendum I), ENCePP protocol template ID, RMP implications, FDA analogue, and submission obligations. Pure decision-tree per EMA GVP rev 4 + EU Regulation 1235/2010 Article 107a. <200ms response.
• `hta_dossier` Pharmacovigilance Plan section — pass pv_classification from pv_classify to hta_dossier and the dossier output now includes a PV Plan section between RoB and CEA. Without it, a one-line "PV plan not provided" note flags the gap so reviewers see what's missing.
• `maic_workflow` orchestrator *(v1.0.6)* — runs the full MAIC discovery+screening pipeline (ITC feasibility + parallel literature_search + screening + RoB + network) in one MCP call. Built for ChatGPT-5.3 surfaces where chaining 5+ tool calls in parallel is unreliable; works equally well from Claude.
• `examples` tool *(v1.0.5)* — pre-filled JSON inputs for heavy-schema tools (CEA, BIA, survival, MAIC, Bucher) plus a maic_workflow_recipe multi-step prompt template for ChatGPT users.
• CMS IRA awareness — when pv_classify is called with US jurisdiction, output explicitly notes that CMS IRA Medicare price-negotiation calculations exclude PV cost data — track those obligations in the regulatory budget, not the HEOR cost-effectiveness model.
• GRADE I²-based inconsistency, GRADE upgrading (Guyatt 2011), Bucher consistency check, EQ-5D 5L baseline-utility-aware impact *(v1.0.4)* — see CHANGELOG.md.
• ChatGPT Custom GPT support *(v1.0.4)* — OpenAPI 3.1 adapter at /api/openapi lets you build a Custom GPT in 5 minutes. See ChatGPT Custom GPT below.
• Surface-tagged analytics *(v1.0.4)* — every tool_call PostHog event carries a surface property (claude_anthropic_web / chatgpt_adapter / claude_desktop / direct_mcp).

See CHANGELOG.md for the full diff.

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Tools (28)

literature_search

Searches across 44 sources in parallel. Every call returns a source selection table showing which of the 44 sources were used and why — essential for HTA audit trails.

Example call:

{

"query": "semaglutide cardiovascular outcomes type 2 diabetes", "sources": ["pubmed", "clinicaltrials", "nice_ta", "cadth_reviews", "icer_reports"], "max_results": 20, "output_format": "text" }

cost_effectiveness_model

Multi-state Markov model (default) or Partitioned Survival Analysis (oncology), following ISPOR good practice and NICE reference case (3.5% discount rate, half-cycle correction). Includes:

• PSA — 1,000–10,000 Monte Carlo iterations, probability cost-effective at WTP thresholds
• OWSA — one-way sensitivity analysis with tornado summary
• CEAC — cost-effectiveness acceptability curve
• EVPI — expected value of perfect information
• WTP assessment — verdict against NHS (£25–35K/QALY, updated April 2026), US payer ($100–150K), societal