second-brain-mcp
Provides read-only access to an Obsidian vault, with tools for indexing, searching, reading notes, and listing recently modified pages while enforcing a page-read budget and path sandbox.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@second-brain-mcpWhat are my notes about the Q3 pricing proposal?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
second-brain-mcp
A read-only MCP server for any Obsidian or plain-markdown vault, with the retrieval protocol enforced by the server instead of requested in prose.
Point it at a folder of markdown notes and every MCP client - Claude Code, Claude Desktop, Cursor, anything - can query that knowledge base through four governed tools. The server physically cannot write, cannot leave the vault directory, and cuts a session off after a hard budget of page reads.
Why
Personal knowledge bases end up welded to one tool. The notes live in Obsidian; the AI assistant that could use them lives somewhere else, so you copy-paste. And when an assistant does get file access, "please read only what you need" is a politeness request, not a rule.
This server fixes both:
One connector, every app. MCP is the USB-C of AI tools - write the vault connector once and any MCP client can use it.
The protocol is law, not a suggestion. Index-first retrieval, a hard page-read budget, read-only access, and a path sandbox are enforced in code. The only operations that exist are the governed ones.
Related MCP server: vault-mcp-bridge
Install
Requires Node.js 18 or newer.
Option A - from npm
claude mcp add second-brain -- npx -y @nanthansr/second-brain-mcp /abs/path/to/your/vaultThat single command registers the server with Claude Code; npx fetches and runs the package automatically. For other clients, see the config blocks below.
Option B - from source
git clone https://github.com/nanthansr/second-brain-mcp
cd second-brain-mcp
npm install && npm run build
npm test # 15-check integration suite - should end with SMOKE PASS
claude mcp add second-brain -- node /abs/path/to/second-brain-mcp/dist/index.js /abs/path/to/your/vaultClaude Desktop
Add to claude_desktop_config.json (Settings → Developer → Edit Config):
{
"mcpServers": {
"second-brain": {
"command": "npx",
"args": ["-y", "@nanthansr/second-brain-mcp", "/abs/path/to/your/vault"]
}
}
}Cursor
Add the same block to ~/.cursor/mcp.json (or Cursor Settings → MCP → Add new server).
No vault handy?
Omit the vault argument entirely and the server serves its bundled fictional demo vault ("Alex Rivera") - useful for trying it in 30 seconds:
claude mcp add second-brain-demo -- npx -y @nanthansr/second-brain-mcpPointing it at your Obsidian vault
Your vault is just a folder - the one you picked when Obsidian said "Open folder as vault". Pass that folder's absolute path as the argument:
OS | Example |
Windows |
|
macOS / Linux |
|
Notes:
An
index.mdat the vault root unlocks the index-first flow (get_index): a catalog page with one line per note. If you don't have one, everything still works - the model falls back tosearch_notes.Obsidian's own config (
.obsidian/) and any other dotfolders are invisible to the server.The server never modifies anything - Obsidian can stay open while it runs.
Usage
Once connected, just ask questions. Typical flows (from a real session against the demo vault):
"What is Alex Rivera working on and who is Sam?" →
get_index→read_note×3 (each stampedread 1/5,read 2/5,read 3/5) → cited answer.
"What changed in my vault this week?" →
list_recent(days: 7)→ dated list, newest first.
"Where do I keep my notes about pricing?" →
search_notes(query: "pricing")→ matching pages with line-numbered snippets, no budget spent.
Clients that support MCP prompts also get vault-retrieval - a slash-command template that pins the model to the index-first protocol for a given question.
What the client gets
Kind | Name | What it does | Budget |
tool |
| Returns | free |
tool |
| Case-insensitive search, returns pages + line-numbered snippets | free |
tool |
| Full content of one page by vault-relative path | counted |
tool |
| Pages modified in the last N days, newest first | free |
resource |
| The index as an MCP resource | free |
prompt |
| The index-first protocol as a reusable prompt template | - |
The intended flow mirrors how a careful human uses a wiki: read the catalog, open the one or two pages that matter, answer with citations. Locating is cheap; reading is budgeted.
Configuration
Setting | How | Default |
Vault path | first CLI argument, or | bundled |
Page read budget |
| 5 per session |
Security model
Read-only by construction. No write, edit, or delete tool exists in the codebase.
Path sandbox. Every path is canonicalized with
path.resolvefirst, then checked against the vault root - traversal attempts (../…) are rejected. Only.mdfiles are readable.Hard page budget. After N
read_notecalls (default 5) the server refuses further reads and tells the model to synthesize from what it has. Failed reads do not consume budget.Size caps. Notes truncate at 50KB; search results and recency lists are capped.
Dotfolders skipped.
.obsidian,.git, and other dotfolders are invisible.Code is public, data is not. The repo contains only server code and a fictional demo vault. Your real vault is whatever folder you mount at runtime; it never leaves your machine.
FAQ
Does my data leave my machine? No. The server runs locally as a child process of your MCP client and reads files from disk. There is no network code in it.
Can it modify or delete my notes? No. There is no tool that writes. This is a property of the code, not a setting.
What happens when the model hits the budget? The 6th read returns an error telling the model to synthesize from the pages it already has. A new conversation gets a fresh budget.
Why did the demo answer talk about "Alex Rivera"? You're on the bundled fictional demo vault. Pass your own vault path as the first argument.
Development
npm run build # tsc -> dist/
npm test # build + 15-check smoke test (spawns the real server over stdio)The smoke test uses the SDK's own client against the compiled server - real protocol, no mocks. It verifies all four tools, the resource, the prompt, path-traversal rejection, and that the read budget refuses the N+1th read. CI runs it on Linux and Windows, Node 20 and 22.
Curious why it's built this way? See docs/design-notes.md - transports, the three MCP primitives, schemas-as-prompts, and the sandbox and budget decisions.
Roadmap
Remote variant (streamable HTTP) so the vault is reachable from hosted clients, with auth
Optional per-folder scoping (serve only
wiki/, hidejournal/)
Contributing
Issues and PRs welcome. Keep the invariants: no write tools, no network calls, the smoke test stays green and unweakened.
License
MIT · Changes in CHANGELOG.md
Available Tools
4 toolsget_indexGet the vault indexA
Returns index.md, the catalog of every page in the vault (one line each). ALWAYS call this first to locate relevant pages, then read only the 1-2 pages that matter. Does not count against the page budget.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the output behavior (one line per page) and adds a meaningful operational trait: 'Does not count against the page budget,' which is beyond what the schema or title conveys. It could mention side-effect-free/read-only nature, but the context strongly implies a safe read.
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?
Three short sentences deliver the core purpose, usage directive, and budget behavior in order of importance. No filler or redundant restating of the title.
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 tool with no output schema, the description fully covers what the agent needs: what it returns, how to use it first, and why it is safe to invoke. There are no missing details that would prevent a correct call.
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 schema coverage is 100%, so there are no parameter semantics to document. The description adds useful meaning about the returned content, which is more than necessary 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?
States a specific verb ('Returns') and resource ('index.md'), and precisely defines the output as 'the catalog of every page in the vault (one line each).' This clearly differentiates it from the sibling tools like search_notes or read_note.
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?
Explicitly directs the agent to 'ALWAYS call this first' and then 'read only the 1-2 pages that matter,' giving a clear workflow. It does not explicitly name search_notes or list_recent as alternatives, but the sequential guidance is strong enough to establish when this tool should be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_recentList recently updated pagesA
Lists pages modified in the last N days (default 7), newest first. Good for 'what changed lately'. Does not count against the page budget.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | Look-back window in days (default 7) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose useful behavioral traits: a default look-back window, newest-first ordering, and that it does not consume page budget. It does not describe the returned page format or any limits beyond the schema, but for a simple read-only list tool this is solid transparency.
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?
Two short sentences carry the core behavior, the primary use case, and a key operational constraint with no filler. The most important information is front-loaded, and every sentence 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 simple tool with one fully documented optional parameter and no output schema, the description covers the essential behaviors: what is listed, the time window, ordering, and budget impact. It could be more complete by describing what fields/details appear in the returned page list, but it is sufficient for an agent to select and invoke the tool correctly.
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 description coverage is 100% and the single 'days' parameter is already documented with type, range, and default. The description only repeats the default window, adding no new semantic meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 clearly states a specific verb ('Lists') and resource ('pages'), with a defined time window and sort order ('last N days (default 7), newest first'). It does not explicitly name or differentiate itself from sibling tools like search_notes or get_index, but the 'recently modified' framing makes its distinct purpose reasonably clear.
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 gives clear usage context with 'Good for what changed lately' and adds a practical benefit ('Does not count against the page budget'). However, it does not mention when not to use it or point to alternative sibling tools, so it stops short of full routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_noteRead one pageA
Returns the full content of one markdown page by vault-relative path (e.g. wiki/people/sam-okafor.md). Counts against the hard page budget of 5 reads per session - locate pages via get_index or search_notes first, then read only what matters.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes | Vault-relative path to a .md file |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and discloses a key non-obvious behavior: the hard page budget of 5 reads per session. It also clarifies that the tool returns full content. It could additionally mention error behavior for missing paths or confirm it is a pure read, but it is already meaningfully transparent.
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?
Two sentences with no filler. The first sentence establishes the core function and path format; the second adds the budget constraint and recommended preceding workflow. Every sentence 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 single-parameter read tool with no output schema, the description is complete: it states what is returned, how to address the resource, and a critical usage constraint. It also routes the agent to the right discovery tools before reading.
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%, so the baseline is 3. The description adds value with a concrete path example ('wiki/people/sam-okafor.md') and reinforces the vault-relative .md semantics, making the expected input format clearer to the agent.
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 states a specific verb ('Returns the full content') and a precise resource ('one markdown page by vault-relative path'). With the title 'Read one page' and sibling tools like search_notes and list_recent, it is immediately distinguishable as a targeted read operation.
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?
It explicitly tells the agent to locate pages via get_index or search_notes first and to 'read only what matters', plus it warns about the hard 5-read budget. It does not explicitly mention list_recent or state when not to use this tool, so it falls just short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_notesSearch the vaultA
Case-insensitive text search across every markdown page. Returns matching pages with line-numbered snippets. Use when the index does not answer 'where does this live'. Does not count against the page budget.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Text to search for (literal, not regex) | |
| max_results | No | Max pages to return (default 10, cap 30) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It discloses case-insensitive matching, global scope, snippet return format, and the useful operational fact that it does not count against the page budget. It does not explicitly state read-only behavior, but this is strongly implied by 'search'.
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?
Three sentences, each carrying distinct value: the core behavior, the return format, and the usage trigger plus budget note. It is front-loaded and free of filler.
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 simple two-parameter search tool with no output schema, the description covers scope, matching behavior, return shape, and when to use it. Nothing essential for correct invocation is missing.
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 description coverage is 100%, so the parameters are already fully documented. The description adds context about case-insensitivity and scope but does not materially extend what the schema provides for query and max_results.
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 states a specific verb ('search') and resource ('every markdown page'), with concrete details like case-insensitivity and line-numbered snippets. It is clearly distinct from sibling tools like get_index, read_note, and list_recent.
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 explicitly says to use it 'when the index does not answer where does this live', giving a clear trigger condition. It does not enumerate exclusions or compare against read_note/list_recent, but the context is unambiguous enough for an agent to route correctly.
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.
4 tool updates
v0.1.0- First observed
get_index - First observed
list_recent - First observed
read_note - First observed
search_notes
TDQS
Scored across 4 tools
Each tool has a clearly distinct role: index for catalog lookup, search for text search, read for content retrieval, and list_recent for recency. The descriptions even specify when to prefer one over another, removing ambiguity.
All tool names use snake_case with an imperative verb first, creating a predictable pattern. list_recent is slightly less parallel than list_recent_notes, but the deviation is minor and does not confuse the set.
Four tools is well-scoped for a retrieval-focused note vault: discovery via index/search/recent and content access via read. Each tool serves a distinct workflow step with no redundancy.
The surface fully covers read-oriented workflows: locating notes, finding content by text, checking recent changes, and reading full pages. The only notable gap is write/edit operations, but the tool descriptions consistently frame this as a read-only vault interface.
Maintenance
Related MCP Connectors
Search and reason over your Obsidian-style Markdown vault, right from ChatGPT.
Agent-native notes, tasks, dev-docs, vaults, sync & handoffs. MCP + OpenAPI dual surface.
Read-only search and Markdown access to liz's public docs, prompts, resources, and an MCP App.
Markdown-based note-taking with a hosted MCP server. Your notes serve you and your AI.
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