Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
MCP gateway: bounded tool shortlists + artifact-backed result firewall. Deterministic, no model.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
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": {
"contextweaver": {
"command": "uvx",
"args": [
"contextweaver",
"mcp",
"serve",
"--config",
"your-value"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add contextweaver -- uvx contextweaver mcp serve --config your-value이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"contextweaver": {
"command": "uvx",
"args": [
"contextweaver",
"mcp",
"serve",
"--config",
"your-value"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 실행 명령어에 값을 넣어야 해서, 설정을 복사한 뒤 직접 채워 주세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"contextweaver": {
"type": "stdio",
"command": "uvx",
"args": [
"contextweaver",
"mcp",
"serve",
"--config",
"your-value"
]
}
}
}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 6일 점검
레지스트리 이름이 GitHub 계정 dgenio 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(0.18.2)은 2개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, FOGA node
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
A Finch OG Agent cell as an MCP server: read the world, dry-run and take ERC-8414 tenders by policy.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Capture an agent's effective capability surface, commit it, and see semantically meaningful changes before deployment.
ContextWeaver is currently testing a deliberately narrow product hypothesis: capability snapshot + semantic drift.
Given an OpenAPI document, a captured MCP tools/list response, or a native ContextWeaver catalog, the D1 experiment produces a deterministic normalized snapshot that you can inspect, verify, and compare with a later candidate. It does not require a model account, a gateway, a tool executor, or the Weaver Stack.
Status: alpha, and specifically a product experiment. The implementation works and is tested; the user-value hypothesis is not yet proven. The project is actively measuring whether independent users keep this workflow after trying it on real projects.
Clone the repository and install that checkout so the maintained example fixtures and the code under evaluation are guaranteed to match:
Commands below use python3, matching the repository's documented default (docs/agent-context/workflows.md). On Windows installations that expose Python through the launcher, replace python3 with py -3; if your environment already exposes the intended interpreter as python, use that consistently.
git clone --depth 1 https://github.com/dgenio/contextweaver.git
cd contextweaver python3 -m pip install .
Run the maintained OpenAPI example:
python3 -m contextweaver.d1 snapshot examples/d1/openapi_before.json --source-type openapi --output ./cw-before.json
python3 -m contextweaver.d1 snapshot examples/d1/openapi_after.json --source-type openapi --output ./cw-after.json python3 -m contextweaver.d1 inspect ./cw-after.json python3 -m contextweaver.d1 verify ./cw-after.json python3 -m contextweaver.d1 diff ./cw-before.json ./cw-after.json
The candidate fixture intentionally:
customer_id required on the existing listInvoices capability;getInvoice capability.The diff separates capability additions/removals from changes to an existing logical capability and reports the structured paths that changed. Contract changes are separated from documentation-only changes. Changes involving fields such as required, type, or enum are flagged as potentially breaking for review.
That flag is intentionally conservative: ContextWeaver does not claim to be a complete JSON-Schema compatibility checker.
Full walkthrough: Capability drift experiment.
python3 -m contextweaver.d1 snapshot ./openapi.yaml --source-type openapi --output ./capabilities.json
python3 -m contextweaver.d1 verify ./capabilities.json
After the API changes:
python3 -m contextweaver.d1 snapshot ./openapi.yaml --source-type openapi --output ./capabilities-candidate.json
python3 -m contextweaver.d1 diff ./capabilities.json ./capabilities-candidate.json
If you already have an MCP tools/list response saved as JSON:
python3 -m contextweaver.d1 snapshot ./tools-list.json \
--source-type mcp \ --output ./capabilities.json
For MCP, D1 compares tools by their upstream logical name so an input-schema edit appears as a change to the same capability rather than an unexplained remove/add pair. The historical schema-sensitive routing ID is retained separately as normalized_id for inspection.
Capturing a live MCP server is a separate operation. snapshot, inspect, diff, and verify do not execute discovered capabilities.
python3 -m contextweaver.d1 snapshot ./catalog.json \
--source-type native \ --output ./capabilities.json
verify meansverify checks the D1 snapshot contract: structure, deterministic ordering, logical-ID uniqueness, and the canonical capability digest.
It is not:
A negative answer is useful evidence for this project. Do not add ContextWeaver just because capability snapshots sound tidy.
Use something simpler when:
If you try D1 and conclude that Git/tests are cheaper, that is a valid product result — please say so.
The current survival experiment asks a stronger question than whether the code works:
Do capability snapshots and semantic drift reports improve a real
review/manual/risk process enough that independent users keep them?
The project distinguishes:
qualified exposure
-> understood the problem -> chose to evaluate -> attempted setup -> reached first useful output -> used on a real project -> retained independently / removed
Stars, forks, downloads, a successful demo, and maintainer-created integrations are not treated as retained adoption.
The controlling product decision is tracked in #758, and the distribution quality gate is #855. Unassisted first success and retention are tracked in #658 and genuine adoption in #551.
ContextWeaver already contains substantial historical runtime functionality. That code still exists and currently shipped behavior should remain truthful and safe, but existing implementation is not evidence that the project should keep expanding it.
Two broader hypotheses are explicitly evidence-first:
During the D1 experiment, the project is not expanding routing sophistication, runtime bundle machinery, memory/session surfaces, framework breadth, gateway scope, vector stores, or model-assisted enrichment without a concrete external blocker or approved falsification experiment.
If you are maintaining an existing integration that uses those historical surfaces, the relevant documentation remains available:
The D1 implementation supports scoped engineering claims such as deterministic snapshot construction under the documented source/adapter contract and structured semantic-diff output. It does not yet support the stronger claim that users need or retain the product.
The historical token-reduction headline is intentionally not used to sell D1. The current evidence-integrity work for those older benchmark claims is tracked in #841.
See Claims & evidence for the claim registry and Capability drift experiment for the exact D1 contract and limitations.
D1 is intentionally exposed through:
python3 -m contextweaver.d1 ...
rather than being promoted immediately into the historical top-level CLI or a large new public Python API. That is deliberate. The experiment should earn a permanent surface through real retained use before the project takes on another compatibility obligation.
ContextWeaver can be used standalone. It has no hard dependency on the sibling Weaver projects.
The wider Weaver Stack contains adjacent experiments/components for planning, execution boundaries, guardrails, lessons, and evaluation. That ecosystem is not required to evaluate D1, and Stack coherence is not a reason to preserve a ContextWeaver feature that does not justify itself independently.
See the Ecosystem map only if you actually need those adjacent responsibilities.
pip install contextweaver
Python 3.10–3.14 are covered by the repository CI matrix.
Current package version: 0.18.2
| Project | Release |
|---|---|
| ContextWeaver (this repo, v0.18.2) | current package release |
The repository is pre-1.0. Prefer the latest supported patch release for bug and security fixes, and check the changelog before relying on historical runtime APIs.
The roadmap is intentionally a product-decision sequence, not a feature queue.
| Milestone | Status | Meaning |
|---|---|---|
| v0.18.1 — D1 survival experiment baseline | ✅ complete | Offline snapshot/inspect/diff/verify exists; user value remains unverified. |
| v0.18.2 | ✅ current (v0.18.2) | D1 survival experiment and release-path recovery |
| D1 distribution gate | 🔬 evidence first | Make the front door understandable, recruit qualified evaluators, measure first success and retention. |
| D1 decision | ⏸ next decision | Continue, shrink further, or kill based on retained value after competent distribution. |
| D2 / D3 | 🧪 conditional | Run only if D1 evidence or independent problem discovery justifies bounded falsification experiments. |
A green CI run does not advance this roadmap by itself.
The most valuable contributions during the survival experiment are narrow and evidence-linked:
Please do not add a framework adapter, routing policy, storage backend, runtime phase, or ecosystem integration solely for completeness.
See CONTRIBUTING.md and AGENTS.md for repository engineering conventions.
See SECURITY.md for supported-ve
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요