Skip to main content
Glama

br8n

Eigenes, portables Arbeitsgedächtnis. Eine Brain-Vorlage aus einfachen Dateien und ein winziger MCP-Server, der es jedem Modell ermöglicht, sie zu lesen.

Alles, was dein Team einem Chat-Tool darüber beigebracht hat, wie die Arbeit erledigt wird, steckt im Login von jemand anderem. Wenn du das Tool wechselst, bist du wieder ein Fremder. Dieses Repo geht den umgekehrten Weg: Das Gedächtnis lebt in Dateien, die dir gehören, und das Modell ist nur ein Leser.

br8n ist die KI-Delivery-Praxis von Branded Mayhem Collective. Das hier ist der offene Teil: das Dateilayout und die Tür. Die gehostete Installation ergänzt Retrieval, Governance und jemanden, der es mit dir betreibt — auf denselben Dateien, die nie ihre Form ändern. br8n.io

Was es hier gibt

  • template/brain/ — das Brain-Layout: how-we-work/, decisions/, exceptions/, handoffs/, voice/. Nur Markdown. Eine Datei, eine Sache. Schreib das Warum, damit das Modell später widersprechen kann.

  • src/ — ein MCP-Server (stdio) mit drei Tools: brain_list, brain_read, brain_search. Die Suche liefert Datei + Zeile, sodass Antworten ihre Quelle zitieren. Keine Vektoren, kein Index, kein Konto. Grep ist der Punkt.

Related MCP server: Universal Memory MCP Server

Verwendung

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 stdio

Claude Desktop / Claude Code / Cursor (beliebiger MCP-Client) — hinzufügen:

{ "mcpServers": { "br8n": { "command": "node", "args": ["/path/to/br8n/dist/cli.js", "/path/to/my-brain"] } } }

Dann frag das Modell etwas, das das Brain weiß. Es antwortet aus der Datei und nennt sie. Wechsle das Modell; dieselbe Antwort, dieselbe Datei.

Warum Dateien

  • Portabel. cp -r brain/ new-machine/ ist die gesamte Migration. Wenn du das nicht kannst, gehört es dir nicht.

  • Einsehbar. Du kannst jedes Byte lesen, das das Modell liest.

  • Modell-agnostisch. Dem Ordner ist es egal, welches Modell auf der anderen Seite von MCP sitzt.

  • Es kann widersprechen. Eine Entscheidung, die mit ihrem Warum gespeichert ist, erlaubt einem Modell zu sagen: „Das widerspricht dem, was du im März entschieden hast." Ein Chatverlauf kann das nicht.

Die Methode ist absichtlich öffentlich

Das Layout und dieser Server sind MIT-lizenziert. Wofür br8n Geld verlangt, ist praktische Arbeit an einem echten Betrieb: herauszufinden, was tatsächlich in den Köpfen der Leute steckt, das Retrieval auf die Rolle zuzuschneiden, es zu betreiben und aktuell zu halten. Wenn du es lieber selbst machst, fang hier an — die meisten sollten das. Der erste Kurs ist kostenlos unter br8n.io/lab.

Nicht verbunden

br8n auf PyPI (eine Context-Capture-Engine von einem anderen Autor) ist nicht dieses Projekt.

MIT © 2026 Branded Mayhem Collective LLC

Veröffentlichung (Maintainer)

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 registry

Available Tools

3 tools
brain_listA
Read-onlyIdempotent

List every markdown file in the brain (relative path, size, last modified).

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.5/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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_readA
Read-onlyIdempotent

Read one file from the brain by relative path (e.g. decisions/2026-03-change-orders.md).

ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesrelative path inside the brain

TDQS

A4.1/5.0
Behavior3/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines3/5

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.2
    • First observedbrain_list
    • First observedbrain_read
    • First observedbrain_search

TDQS

A4.4/5.0

Scored across 3 tools

Disambiguation5/5

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.

Naming Consistency5/5

All tools consistently use the 'brain_' prefix with a simple verb pattern (list/read/search), making the API predictable and easy to navigate.

Tool Count5/5

Three tools is minimal but well-scoped for a read-only markdown knowledge base. Each tool serves a necessary and non-redundant function.

Completeness4/5

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

ActivitySlowing
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers