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Provenance-aware computational epitope research tools with reproducible offline demonstrations.
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쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
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위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"immunograph": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/ajey95/immuno-graph:0.2.1",
"node",
"dist/index.js"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add immunograph -- docker run -i --rm ghcr.io/ajey95/immuno-graph:0.2.1 node dist/index.js프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"immunograph": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/ajey95/immuno-graph:0.2.1",
"node",
"dist/index.js"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"immunograph": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/ajey95/immuno-graph:0.2.1",
"node",
"dist/index.js"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 Ajey95 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.2.1)은 6일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Docker도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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A standard Model Context Protocol server for computational epitope research. Call 46 typed tools for sequence processing, immune evidence, structure and chemistry analysis, docking, and reviewable exports. Results carry provenance so a live provider result stays distinct from a fixture or synthetic demonstration.
Problem · Who it is for · User flow · Get started · 3D outcomes · All 46 tools · Contribute
[!IMPORTANT]
ImmunoGraph supports computational research, not clinical or experimental validation. A predicted epitope, docking pose, score, or exported 3D model does not establish binding, vaccine efficacy, or safety. Fixture and synthetic results are demonstrations and are labelled as such.
An epitope research workflow crosses different kinds of software: sequence processing, immune predictors, HLA coverage, structure databases, molecular preparation, docking, interaction analysis, and reporting. Each step has its own input format, runtime, and scientific assumptions. When an LLM or research application chains them together, it must also keep track of *which inputs and methods produced each result*. A plausible looking score or 3D view is easy to misread if its source is missing.
ImmunoGraph gives an MCP host one typed interface for these steps. The host can discover and call individual tools, pass outputs between them, and inspect explicit source labels and exported bytes. This reduces custom integration code for a reviewable computational workflow; it does not replace scientific judgment or validate a prediction by itself.
| Audience | Typical use |
|---|---|
| Computational immunology and vaccine research teams | Assemble and review sequence, MHC, B-cell, coverage, and structural evidence for candidate exploration |
| Structural bioinformatics and medicinal chemistry researchers | Prepare coordinates, run configured docking tools, inspect poses and contacts, and export the actual files |
| Developers building LLM research assistants | Expose typed scientific operations through standard MCP instead of writing a separate adapter for every step |
| Educators and reproducibility reviewers | Demonstrate a workflow with clearly marked fixtures, then inspect method boundaries, provenance, and artifacts |
The outputs are for research and education. Clinical decisions, efficacy claims, and experimental validation require independent methods and evidence.
| Explore | Analyze | Review and share |
|---|---|---|
| Validate FASTA, generate peptide windows, retrieve PDB structures and PubChem compounds | Call enabled IEDB predictors; calculate consensus and coverage; run RDKit, FreeSASA, fpocket, Meeko, Vina, and PLIP where installed | Rank caller-supplied evidence; export CSV, PDB, SDF, PDBQT, docking poses, Mol* or docking viewers, and a checksummed research ZIP |
This is one server with 46 tools, not 46 independent live data providers. Each tool is callable on its own. A client passes results between calls when building a workflow.
The researcher works in an MCP-enabled chat or application. The client chooses the calls and carries evidence forward; ImmunoGraph executes each requested operation and returns a typed result.
| Step | Researcher or client action | ImmunoGraph response |
|---|---|---|
| 1. Connect | Add the stdio server to an MCP host; enable only the live connectors needed for the study | The host discovers 46 tools and their input schemas |
| 2. Define inputs | Provide a FASTA sequence and study criteria | validate_sequence checks and hashes the sequence; generate_candidate_peptides enumerates real peptide windows |
| 3. Gather evidence | Request available MHC-I/II, B-cell, population, or other calculations | Each call returns scores or a structured failure with source status; live predictors require their configured service |
| 4. Prioritize | Pass selected evidence, thresholds, and coverage data into the scoring and constraint tools | Normalized scores, consensus, exclusions, and ranked candidates depend on the supplied evidence |
| 5. Inspect structure | Optionally fetch PDB/compound data and run accessibility, pocket, ligand, docking, or contact tools | Coordinate-based outputs, pose files, 3D viewer HTML, and analysis data where the scientific runtime is installed |
| 6. Review and export | Inspect source labels and parameters, then request reports, CSV, trace, or research ZIP | File bytes with lengths and SHA-256 hashes for independent saving and verification |
This flow is modular: a user can call one tool without running every stage. run_agentic_workflow executes caller-supplied, approved tool calls; without those calls it returns a plan rather than silently launching a full study. See architecture and the tool reality matrix for exact execution boundaries.
Node.js 20 or newer is required. The versioned tarball is hosted on GitHub and installs with npm; the immunograph-mcp name is not yet published on the npm registry.
npm install --global https://github.com/Ajey95/immuno-graph/releases/download/v0.2.1/immunograph-mcp-0.2.1.tgz
Register the installed command in an MCP host that supports stdio servers:
{
"mcpServers": { "immunograph": { "command": "immunograph-mcp" } } }
The server speaks MCP on stdio, so launch it through your host. If the host cannot find the globally installed command, configure its absolute path. The GitHub release also includes the source and a SHA-256 checksum for the tarball. The downloaded public tarball matched that checksum.
The official MCP Registry entry points to the public OCI image. For a local stdio host, use:
{
"mcpServers": { "immunograph": { "command": "docker", "args": [ "run", "--rm", "-i", "ghcr.io/ajey95/immuno-graph:0.2.1", "node", "dist/index.js" ] } } }
The container also supports guarded Streamable HTTP at /mcp. See HTTP deployment for its required security settings.
git clone https://github.com/Ajey95/immuno-graph.git
cd immuno-graph npm ci npm run check npm start
npm start serves stdio MCP. npm run check runs the software tests, build, all-tool example calls, semantic audit, dependency audit, and package dry run. For a host, point command at node and args at the absolute path to dist/index.js.
flowchart LR
A["FASTA · PDB · SMILES"] --> H["MCP host"] H --> S["ImmunoGraph 46 typed tools"] S --> D["Local calculations and exact fixtures"] S --> L["Optional live connectors"] D --> R["Results + provenance"] L --> R R --> O["Reports · 3D files · ZIP"]
The host chooses which tools to call. ImmunoGraph validates typed inputs, executes a handler, and returns structured success or failure data with run metadata and source labels. The server does not silently run a full study or treat fixture values as live evidence. See architecture for transport and module details.
| Concern | Implementation | Why it matters |
|---|---|---|
| Client interoperability | Official MCP SDK; stdio for local hosts and guarded Streamable HTTP for deployments | One discoverable tool contract works across MCP-capable clients |
| Input and result contracts | Zod schemas, typed handlers, structured MCP results, explicit errors | Clients can validate arguments and handle failures programmatically |
| Scientific provenance | Source status, method/run metadata, and versioned exact-match fixtures | Reviewers can distinguish configured live work from offline demonstrations |
| Export integrity | Inline artifact bytes, byte lengths, and SHA-256 digests | A host can save actual PDB/PDBQT/CSV/ZIP files and verify them |
| Operational boundaries | Optional connectors and local scientific binaries are configured separately | Missing providers fail explicitly or use only the requested, labelled fallback |
The registry entry points to the published OCI image. Optional scientific binaries, model downloads, and third-party service availability remain separate runtime requirements.
flowchart LR
FASTA["Protein FASTA"] --> V["Peptide windows"] V --> E["Immune evidence"] E --> R["Constraints + ranking"] R --> X["Reviewable exports"] E -.->|Optional| S["Structure + docking"] S -.-> X
These are separate MCP calls. Rankings and reports depend on the evidence the client supplies. With live prediction disabled, an arbitrary new protein has no automatic scientific binding result.
Configured live tools can use actual coordinates rather than decorative models:
flowchart LR
P["Protein PDB"] --> R["Meeko receptor PDBQT"] L["Compound / SMILES"] --> M["RDKit + Meeko ligand SDF + PDBQT"] R --> V["AutoDock Vina"] M --> V V --> POSE["Pose PDBQT complex PDB"] POSE --> VIEW["3D viewer HTML"] POSE --> PLIP["PLIP contacts"] POSE --> ZIP["Research ZIP"]
1IEP + imatinib: exported top Vina pose. This figure is rendered from a saved complex PDB produced by a LIVE_ONLY MCP docking call. Panel A traces the protein Cα coordinates and shows the ligand; panel B shows the ligand and protein atoms within 5 Å. The render script reproduces the image from those coordinates. This is a docking capability example, not an epitope candidate; the pose score is a computational estimate, not measured affinity.
1UYD + PubChem CID 2244: three computed poses. This figure comes from the live docking research ZIP, which includes the receptor, ligand, Vina poses, complex, and image. Its run provenance and summary identify the sources and nine generated poses; the image shows three of them. The callouts show distances calculated from the coordinates; dashed polar contacts are inferred geometry, not experimentally established bo
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