Context7
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作者による説明(英語の原文)
Provenance-aware computational epitope research tools with reproducible offline demonstrations.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"immunograph": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/ajey95/immuno-graph:0.2.1",
"node",
"dist/index.js"
]
}
}
}ターミナルでこれを実行します
claude mcp add immunograph -- docker run -i --rm ghcr.io/ajey95/immuno-graph:0.2.1 node dist/index.jsプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"immunograph": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/ajey95/immuno-graph:0.2.1",
"node",
"dist/index.js"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"immunograph": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/ajey95/immuno-graph:0.2.1",
"node",
"dist/index.js"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント Ajey95 で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(0.2.1)がレジストリに届いたのは6 日前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにDockerも必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
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作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
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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