Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Read-only CharacterCheck MCP server for deterministic character context with provenance and trust.
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": {
"charactercheck": {
"command": "uvx",
"args": [
"charactercheck"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add charactercheck -- uvx charactercheck프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"charactercheck": {
"command": "uvx",
"args": [
"charactercheck"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"charactercheck": {
"type": "stdio",
"command": "uvx",
"args": [
"charactercheck"
]
}
}
}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 chaoz23 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(0.7.0)은 3개월 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, Reelsaw
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.
Drive a self-hosted Reelsaw: turn long videos into vertical clips, edit, re-render and export them.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Agents start here → [SKILL.md](SKILL.md) — when to call this, worked examples, MUST/MUST NOTs. Family contract: FAMILY.md.
CharacterCheck is an experimental, read-only compiler for selected D&D Beyond character-sheet fields. It turns a public share, saved character-service JSON, or versioned snapshot into deterministic mechanical context with provenance and named findings.
Current release: 0.7.0. Pin
charactercheck==0.7.0when installation
must be reproducible. Verify the installed package version and each field's
trust state; a version string alone is not proof that a value is supported.
It is not a complete or rules-authoritative character validator. Its output is not encounter, world, or session state and does not prove that an action is legal. Unknown upstream fields and unsupported restrictions can affect results; read the trust and finding data before using a value.
Python 3.9 or newer is required. The runtime has no third-party dependencies.
git clone https://github.com/chaoz23/charactercheck
cd charactercheck python3 -m charactercheck selftest python3 -m charactercheck derive examples/sample-character.json --brief
The bundled example is project-authored synthetic data. The smoke test checks this checkout and selected derivations; it is not evidence of complete D&D rules correctness.
Install the published package into a virtual environment for the console entry points:
python3 -m venv .venv
.venv/bin/python -m pip install charactercheck==0.7.0 .venv/bin/charactercheck derive examples/sample-character.json --brief
The direct python3 -m charactercheck path from a clone has no runtime dependency install. An editable pip install may bootstrap the setuptools build frontend if the environment does not already provide it. See MIGRATING.md before comparing 0.7.0 output or MCP surfaces with 0.6.x.
For an explicitly public character:
python3 -m charactercheck derive https://www.dndbeyond.com/characters/<id>
Public sharing permits retrieval at that moment; it does not establish consent for publication, recurring tests, persistence, model training, or reuse of persona and third-party narrative content.
CharacterCheck requests no credentials. For a private sheet, use an authorized saved character-service JSON file through the local CLI or library; do not pass cookies, tokens, or session data.
CharacterCheck currently supports mechanical, read-only preparation for a human or AI-controlled DM, PC, or NPC. It observes a character source and emits context or reconciliation candidates. It exposes no mutation tools and does not become the authority for HP, conditions, concentration, expended resources, equipment declarations, DM rulings, encounter state, or world state.
The human principal remains responsible for access and use. An AI actor must not infer permission, role, or visibility from sheet contents or a tool argument. Where a value is marked confirm, unsupported, unknown, or invalid, the agent should ask the named human authority or decline to use the value. Manual play without CharacterCheck remains the fallback.
CharacterCheck is being developed as a versioned D&D Beyond-to-agent compiler and conformance facade, not a new complete 5e rules engine. The strict source/snapshot/privacy/trust layer remains local. Adapter vocabulary is seeded from a pinned offline DDB config registry that was independently observed and cross-checked against the MIT-licensed DDB Importer fallback. The production runtime never fetches that config endpoint. The distributed registry is a purpose-limited allowlist for current evaluator branches—not a copy of DDB's full configuration or third-party catalog vocabulary.
Registry membership establishes only that an upstream ID/name was observed; it does not prove that CharacterCheck implements its mechanics. Bounded unknown IDs are retained as opaque adapter evidence, fixed semantic-gap codes prevent default calculations, and unsafe formula/property prose is omitted. Current weapon proficiency/hand state and unimplemented property semantics therefore remain explicitly unsupported even when a provisional attack line can be rendered.
The next mechanics work is a differential conformance spike using exact SRD revisions, narrow MIT-compatible DDB Importer evidence, and Foundry dnd5e as a versioned model/behavior oracle. DDB/displayed and third-party calculated values are attributed claims, not unquestionable truth; disagreement must become a non-trusted conflict. See ECOSYSTEM_CONFORMANCE.md SOURCE_FIELD_ROUTING.md, and THIRD_PARTY_NOTICES.md.
The observed DDB service/config surfaces remain undocumented and unsupported as public APIs. Their technical availability does not grant commercial access, content rights, stability, or consent. A commercial build needs a licensed or counsel-approved input/update path and should prefer an authorized user/host export until then.
The input parser accepts exactly:
dndbeyond.com or www.dndbeyond.com whose path is exactly /characters/ ;CharacterSnapshotV1 JSON file.Local symbolic links are rejected. Missing paths are not reinterpreted as IDs. Raw local character files and HTTP responses are bounded to 8 MiB; a local CharacterSnapshotV1 envelope may be up to 16 MiB so a maximum-size filtered source can round-trip with its metadata. JSON also has depth, node, string, collection, inventory, modifier, and container-traversal limits.
| Command | Current contract |
|---|---|
derive | Selected derived fields, provenance, findings, and trust routing |
derive --brief | Deterministic chat-sized summary; still subject to findings |
derive --table-evaluation | Value-free, deterministic, self-attested table.evaluation/1.0 assessment envelope |
stance | Envelope containing stance, canonical assessment, trust, fields, and observation metadata |
report | Canonical field/trust assessments plus findings and identified feature names |
qa [--full] | 100-question Coverage Inventory pairing each answer with a closed field state; not a validity score |
seatpack | Privacy-minimized read-only character context |
intake | Pre-session context plus questions and authority boundaries |
quiz | Settlement and sheet-specific finding questions; non-trusted values never become answer keys |
snapshot | Export a versioned, integrity-checked observation |
diff --baseline snapshot.json | Classify supported changes; mark omitted-source comparisons indeterminate |
doctor [ref] [--json] | Diagnose runtime/network/source access without echoing the ref |
selftest | Offline installation and selected-derivation smoke test |
--pipe reads refs from standard input. charactercheck --schema emits the machine-readable CLI contract.
--table-evaluation is also accepted by report. It projects canonical field states and source/policy digests, never character values. Trusted fields count as evaluated; unsupported, unknown, invalid, and non-authority confirmation states fail closed. Mutable player-authority fields are outside the evaluator's scope and become advisories requiring player/session-host reconciliation. The envelope is always self_attested; it is not proof of source identity, freshness, table role, encounter state, or action legality.
The Coverage Inventory pairs each of its 100 answers with the corresponding human-readable D&D 2024 character-sheet lookup question. Structured rows carry number, field, question, state, value, and content_trust. The question catalog is an organizational contract, not an assertion that the supplied JSON Schema's value types or the complete 2024 rules are implemented. An answer remains unusable when its row is unsupported, unknown, or invalid; mutable confirm rows remain player/session-host authority.
The settlement quiz also includes each sheet-specific lint question exactly once. Those prompts never receive an expected answer when their affected family is not trusted. Account identity and roleplay/persona questions remain privacy-omitted from default output.
Capture a baseline and compare a later observation:
python3 -m charactercheck snapshot examples/sample-character.json > baseline.json
python3 -m charactercheck diff examples/sample-character.json --baseline baseline.json
CharacterSnapshotV1 records the adapter, source ID, source-schema fingerprint (which commits to the pinned adapter registry and semantic-gap contract), observation time, engine/rules profile, privacy classification, hashes, coverage, and a snapshot ID. Canonical derived metadata also exposes the adapter-registry fingerprint directly. Account identifiers and linked images are always removed. Persona is excluded by default. Snapshots do not migrate silently; unsupported versions fail with snapshot_schema. Observation times use canonical UTC RFC 3339 (...Z) syntax; they describe caller/source observation order, not a trusted timestamp or proof of freshness.
The hash meanings are intentionally distinct:
source.normalized_data_hash is the canonical hash of the default privacy-filtered mechanical character. Projections use it as source_revision; it deliberately excludes persona and every omitted field.source.snapshot_character_hash hashes the exact filtered character stored in the snapshot, so an explicitly persona-inclusive snapshot can differ from the default mechanical revision.meta.snapshot_id covers the complete snapshot envelope except for the ID field itself, including observation metadata, privacy declarations, coverage, and the stored character.These hashes are deterministic integrity/content identifiers, not signatures, attestations, or proof of source authenticity. A party able to edit a snapshot can recompute unkeyed hashes. Keep baselines in a trusted store and let an authenticated session host bind them to principals and audit history.
Snapshot filtering is closed at the top level and inside supported nested objects. source.coverage contains three booleans: unclassified_top_level_omitted, unclassified_nested_omitted, and semantic_values_omitted, plus a sorted scoped_mechanical_omissions list of canonical family names. It never carries omitted source names or values. Reviewed display/provenance omissions are trust-neutral; reviewed mechanical omissions add source:scoped-fields-omitted and route only listed families to unsupported. The first two booleans have unknown mechanical scope; derive adds the static source:unclassified-fields-omitted finding and routes every mechanical family to unknown. The third records unsafe semantic text removed after a fixed, field-scoped _semanticGaps code was retained; the item-semantic ledger routes the affected family to unsupported without copying the text. The same coverage appears as meta.source_coverage; a downstream view may not turn any incomplete observation back into trusted.
Diff is partial by design. Its coverage.classified list enumerates the coarse source families it can compare. When either of two distinct snapshots says an unclassified or reviewed scoped field o
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요