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Aegis MCP Server

@fuwasegu

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基本情報

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その他

トランスポート

stdio

作者

fuwasegu

投稿者

Fuwasegu

Aegis MCP Server について

A deterministic context compiler for AI coding agents. Aegis replaces RAG with a Directed Acyclic Graph (DAG) to strictly enforce software architecture and reduce token consumption by up to 12x.

#context#document#cursor#claudecode#codex#knowledge

Aegis MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "aegis": {
      "command": "npx",
      "args": [
        "-y",
        "@fuwasegu/aegis",
        "--surface",
        "agent"
      ]
    },
    "aegis-admin": {
      "command": "npx",
      "args": [
        "-y",
        "@fuwasegu/aegis",
        "--surface",
        "admin"
      ]
    }
  }
}

Aegis MCP Server のツール

6

Compile deterministic context for target files. Returns base documents, resolution path, and templates. v2: delivery-aware with budget control.

Record an observation event. Writes to Observation Layer only (never Canonical).

Retrieve details of a past compile_context invocation.

List distinct intent tags from tag_mappings (approved-resolvable only) with knowledge_version and a SHA-256 tag_catalog_hash of the tag list for client-side caching.

Read-only workspace snapshot: recent compile regions (from compile_log), unresolved compile_miss observations without proposals, and pending proposal count. Does not mutate Canonical Knowledge.

Analyze a project and generate an initialization preview. Does not modify Canonical.

概要

What is Aegis?

Aegis is a deterministic context compiler for AI coding agents (e.g., Cursor, Claude Code, Codex). It replaces probabilistic search with a Directed Acyclic Graph (DAG) backed by SQLite to map file paths and layers directly to Markdown documentation, ensuring agents receive exact architectural context for each editing task.

How to use Aegis?

After installing and configuring the server, agents invoke Aegis before editing a file; it deterministically traverses the dependency graph and compiles the required context. The exact setup and invocation commands are not detailed beyond the paradigm described.

Key features of Aegis

  • Deterministic Context Routing (same input, same context, reproducible).
  • Transitive Dependency Resolution (automatically fetches dependent guidelines).
  • Dual Surface Architecture (Agent read-only context vs. Admin human review).
  • Human-in-the-Loop Knowledge Evolution (proposes DAG updates, approved by humans).
  • Massive Token Efficiency (12x reduction in token consumption, 3.5x faster responses in benchmarks).
  • Optional Local SLM Expansion (node-llama-cpp engine for semantic intent extraction).

Use cases of Aegis

  • AI coding agents needing architectural guidelines for a specific file or layer.
  • Preventing AI hallucinated architectures by feeding deterministic context.
  • Reducing token waste from agents endlessly grepping repositories.
  • Maintaining codebase integrity with a “Tech Lead” that forces rules before edits.
  • Enabling agent self-review and human-approved knowledge base updates.

よくある質問

1

How does Aegis differ from traditional RAG?

Traditional RAG suffers from a “semantic gap”—searching by feature keywords fails to retrieve abstract architectural rules. Aegis uses deterministic compilation via a DAG, not probabilistic vector embeddings.

2

What are the runtime dependencies?

Aegis uses a SQLite-backed DAG. It optionally supports node-llama-cpp (Qwen) for local SLM expansion, but this is opt-in.

3

Where does the context data live?

All rules and dependency mappings are stored in a SQLite database that backs the DAG.

4

Can agents update the knowledge base?

Agents can trigger `aegis_observe` to report missing rules, but canonical knowledge updates require human approval on the admin surface.

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