br8n
Officialbr8n
소유 가능한 휴대용 작업 메모리. 일반 파일로 된 브레인 템플릿과 어떤 모델이든 읽을 수 있게 해주는 작은 MCP 서버입니다.
팀이 채팅 도구에 가르쳐 둔 업무 방식에 대한 모든 것이 남의 로그인 안에 갇혀 있습니다. 도구를 바꾸면 다시 낯선 사람이 됩니다. 이 저장소는 반대 방향입니다. 메모리는 당신이 소유한 파일에 있고, 모델은 그저 읽는 사람일 뿐입니다.
br8n은 Branded Mayhem Collective의 AI 전달 실무입니다. 이곳은 공개된 부분, 즉 파일 레이아웃과 문입니다. 호스팅 설치 버전은 검색, 거버넌스, 그리고 같은 파일(형태가 절대 바뀌지 않는)을 함께 운영해 줄 사람을 추가합니다. br8n.io
여기 있는 것들
template/brain/— 브레인 레이아웃:how-we-work/,decisions/,exceptions/,handoffs/,voice/. 마크다운만 사용합니다. 파일 하나에 하나의 주제. 나중에 모델이 반박할 수 있도록 이유를 적으세요.src/— 세 가지 도구(brain_list,brain_read,brain_search)를 갖춘 MCP 서버(stdio)입니다. 검색은 파일과 줄 번호를 반환하므로 답변이 출처를 인용합니다. 벡터도, 인덱스도, 계정도 없습니다. grep이 핵심입니다.
Related MCP server: Universal Memory MCP Server
사용 방법
git clone https://github.com/Branded-Mayhem-Collective-LLC/br8n
cd br8n && npm install && npm run build
cp -r template/brain ~/my-brain # now write in it
node dist/cli.js ~/my-brain # MCP server on stdioClaude Desktop / Claude Code / Cursor (모든 MCP 클라이언트) — 추가:
{ "mcpServers": { "br8n": { "command": "node", "args": ["/path/to/br8n/dist/cli.js", "/path/to/my-brain"] } } }그런 다음 브레인이 알고 있는 것을 모델에게 물어보세요. 모델은 파일에서 답하고 그 이름을 밝힙니다. 모델을 바꿔도 같은 답, 같은 파일입니다.
왜 파일인가
휴대성.
cp -r brain/ new-machine/이 전체 마이그레이션입니다. 그렇게 할 수 없다면 당신이 소유한 것이 아닙니다.검사 가능. 모델이 읽는 모든 바이트를 직접 읽을 수 있습니다.
모델 무관. 폴더는 MCP 반대편에 어떤 모델이 있는지 신경 쓰지 않습니다.
반박 가능. 이유와 함께 저장된 결정 덕분에 모델이 "이건 3월에 내린 결정과 충돌합니다"라고 말할 수 있습니다. 채팅 기록은 그럴 수 없습니다.
방법은 의도적으로 공개되어 있습니다
레이아웃과 이 서버는 MIT 라이선스입니다. br8n이 비용을 받는 것은 실제 운영에 손을 대는 일입니다. 사람들 머릿속에 실제로 있는 것을 이끌어내고, 역할에 맞게 검색을 구성하고, 운영하며, 최신 상태로 유지하는 일입니다. 직접 하고 싶다면 여기서 시작하세요. 대부분의 사람이 그래야 합니다. 무료 첫 강좌는 br8n.io/lab에 있습니다.
관련 없음
PyPI의 br8n(다른 저자의 컨텍스트 캡처 엔진)은 이 프로젝트가 아닙니다.
MIT © 2026 Branded Mayhem Collective LLC
배포 (관리자용)
npm login && npm publish --access public # 1. claims `br8n` on npm; package.json carries mcpName
mcp-publisher login github # 2. GitHub device-flow auth (org member)
mcp-publisher publish # 3. lists io.github.Branded-Mayhem-Collective-LLC/br8n in the official MCP registryAvailable Tools
3 toolsbrain_listARead-onlyIdempotent
List every markdown file in the brain (relative path, size, last modified).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds value by specifying the exact scope ('every markdown file') and the output fields, which clarifies what the tool returns without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words. It front-loads the action and resource, then appends the relevant output fields in parentheses, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only list operation with annotations covering safety, the description is complete. It states what is listed, the scope, and the returned fields; no additional information is needed for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so the baseline is 4. The description adds no parameter details, but none are needed; it instead describes the output characteristics, which is more useful for a parameterless tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and clearly identifies the resource ('every markdown file in the brain') plus the fields returned (relative path, size, last modified). This distinguishes it from siblings brain_read and brain_search, which imply reading or searching rather than enumerating.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'List every markdown file' provides clear context for when to use the tool: when the agent needs an inventory of all markdown files. It does not explicitly name alternatives or exclusions, so it stops short of a 5, but the intended usage is evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
brain_readARead-onlyIdempotent
Read one file from the brain by relative path (e.g. decisions/2026-03-change-orders.md).
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | relative path inside the brain |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the scope of reading exactly one file and the relative-path constraint, but provides no details on error behavior or return format. These additions are consistent with annotations but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence with action front-loaded, a precise resource, and a helpful example. No filler or redundancy; every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter, no output schema, and annotations covering side effects, the description is complete. The example and relative-path wording give the agent everything needed to call it correctly, and sibling tools provide surrounding context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the schema already describes 'path' as a relative path, so the baseline is 3. The description goes slightly beyond by giving a concrete example (decisions/2026-03-change-orders.md), which clarifies the expected format and nested structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Read' with a clear resource ('one file from the brain') and a method ('by relative path') plus a concrete example. This distinguishes it naturally from siblings brain_list and brain_search without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage—when you need the contents of a specific file by path—but does not explicitly contrast with brain_list or brain_search, nor does it state when not to use this tool. The context is clear enough but exclusions/alternatives are left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
brain_searchARead-onlyIdempotent
Literal, case-insensitive search across the brain. Returns file, line number and the matching line, so answers can cite the source.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations indicating readOnlyHint=true and destructiveHint=false, the safety profile is already clear. The description adds useful behavioral details: the search is literal and case-insensitive, and the tool returns source-citing output. However, it does not mention limits or edge cases like pagination or behavior with no matches, which would be extra context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is one focused sentence that front-loads the key distinction ('Literal, case-insensitive search') and immediately states the return value. Every word earns its place; no fluff or redundant restatement of the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a simple read-only search with annotations covering safety and idempotence, the description is largely complete. The main missing context is the sibling differentiation and explicit behavior for the 'limit' parameter, but the tool's simplicity and annotations reduce the burden. It does not need to explain return values in detail because it already states them.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema documents only the parameter names and types; description coverage is 0%. The description adds meaning by stating that the search returns file, line number, and matching line, which clarifies the 'query' parameter's effect. It doesn't explain the 'limit' parameter in detail, but a limit's purpose is fairly evident from its integer type and range. A score of 4 is appropriate because the description compensates for the schema's lack of semantic detail on what a query produces.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('search') and resource ('the brain'), and explicitly states what it returns (file, line number, and the matching line). It clearly distinguishes itself from siblings like brain_list and brain_read by framing itself as a search operation rather than listing or reading.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for locating specific content within the brain, which is distinct from brain_list and brain_read, but it does not explicitly state when not to use it or name alternatives. The phrase 'Literal, case-insensitive search' gives some context on when it is appropriate, but it could more explicitly contrast with sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
v0.1.2- First observed
brain_list - First observed
brain_read - First observed
brain_search
TDQS
Scored across 3 tools
Each tool targets a distinct operation: listing all files, reading a specific file, and searching content. There is no overlap or ambiguity in their purposes.
All tools consistently use the 'brain_' prefix with a simple verb pattern (list/read/search), making the API predictable and easy to navigate.
Three tools is minimal but well-scoped for a read-only markdown knowledge base. Each tool serves a necessary and non-redundant function.
The set fully covers the core retrieval workflows: browsing the structure, reading files, and searching content. Write or management operations are absent, but they appear outside the server's stated read-only scope.
Maintenance
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- AlicenseNot gradedqualityBmaintenanceMCP server that turns a git-versioned Markdown vault into a queryable memory for AI agents, providing tools like brain_search, brain_read, and brain_neighbors.Apache 2.0