Ein MCP-Server, der einem KI-Agenten einen durchsuchbaren Vault für Markdown-Notizen bereitstellt: sechs Tools für ein flaches Verzeichnis von .md-Dateien mit YAML-artigem Frontmatter.
Notizen bleiben auf der Festplatte als schlichtes Markdown erhalten. Nichts ist in einer Datenbank eingesperrt, sodass dieselben Dateien mit deinem Editor, grep und git funktionieren.
~/notes/
├── pad-thai.md
├── sourdough-starter.md
└── weekly-review.mdTool | Beschreibung |
| Erstellt eine Notiz. Gibt den generierten Slug zurück. |
| Liest eine Notiz vollständig anhand des Slugs. |
| Zusammenfassungen (ohne Text), neueste zuerst, optional nach Tag gefiltert. |
| Nach Relevanz sortierte Volltextsuche mit Snippets. |
| Aktualisiert Titel / Text / Tags. Der Slug ändert sich nie. |
| Löscht eine Notiz anhand des Slugs. |
Related MCP server: Memory MCP Server
npm install
npm run buildFüge es zur Server-Konfiguration deines Clients hinzu — für Claude Desktop die claude_desktop_config.json:
{
"mcpServers": {
"notes": {
"command": "node",
"args": ["/absolute/path/to/mcp-notes-server/dist/src/index.js", "--vault", "/absolute/path/to/notes"]
}
}
}Das Vault-Verzeichnis wird in dieser Reihenfolge ermittelt: --vault <dir>, dann $NOTES_VAULT, dann ~/notes. Es wird beim Start erstellt, falls es nicht existiert.
Slugs sind die Identität, und sie werden validiert. Eine Notiz liegt unter <vault>/<slug>.md, und jeder Slug wird gegen /^[a-z0-9]+(?:-[a-z0-9]+)*$/ geprüft, bevor er das Dateisystem erreicht. Genau das macht read_note({slug: "../../.ssh/id_rsa"}) unmöglich statt bloß unwahrscheinlich — eine Allowlist, kein Escaping-Durchlauf. Slugs sind zudem über Updates hinweg stabil: Die Datei bei einer Titeländerung umzubenennen würde jeden Slug ungültig machen, den das Modell noch von einem früheren Tool-Aufruf hat.
Tool-Fehler sind Daten, keine Ausnahmen. „Keine Notiz mit Slug X“ kommt als normales Tool-Ergebnis mit isError: true zurück, sodass das Modell es liest und sich selbst korrigiert. Wenn der Fehler geworfen würde, sähe der Client einen Protokollfehler, von dem sich das Modell nicht erholen kann. Echte Bugs (alles, was kein VaultError ist) werden weiterhin geworfen, damit sie laut und deutlich bleiben.
Die Suche basiert auf gewichteter Termhäufigkeit. Ein Begriff im Titel zählt dreifach und ein Begriff in einem Tag zählt doppelt, sodass die Suche nach sourdough eine Notiz über Sauerteig höher einstuft als eine, die ihn nur beiläufig erwähnt. Sie ist bewusst einfach gehalten — kein Index, der synchron gehalten werden muss, und ein Vault mit ein paar tausend Notizen wird in Millisekunden durchsucht.
Der Vault weiß nichts über MCP. src/vault.ts ist einfacher Dateisystem-Code, src/server.ts ist die MCP-Anbindung und src/index.ts ist der stdio-Einstiegspunkt. Diese Trennung ist der Grund, warum sich der Vault direkt testen lässt und der Server über einen echten MCP-Client auf einem In-Memory-Transport getestet werden kann — ohne Subprozess und ohne Mocks.
npm test # 46 tests, vitest
npm run typecheck # tsc --noEmit
npm run build # emit to dist/Die Tests decken den Vault direkt ab (test/vault.test.ts) und den Server End-to-End über einen echten MCP-Client auf InMemoryTransport (test/server.test.ts), sodass Tool-Schemas, Argumentvalidierung und Ergebnisstrukturen alle geprüft werden — nicht nur die dahinterliegende Logik.
MIT
Create a new markdown note. Returns the generated slug, which is the identifier every other tool takes. Slugs are derived from the title and de-duplicated automatically.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | Markdown body of the note. | |
| tags | No | Lowercase topic tags used for filtering, e.g. ['recipes', 'thai']. | |
| title | Yes | Human-readable title for the note. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare readOnlyHint=false and idempotentHint=false, so the description carries the burden of explaining side effects. It discloses that a slug is generated, derived from the titlehol and de-duplicated automatically, which is beyond what annotations convey. It doesn't mention auth or rate limits, but for this simple create tool that's acceptable.
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 zero fluff. The key facts (creates note, returns slug, slug derivation) are all present and front-loaded. No wasted words.
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?
Complete for a two-parameter create tool. It explains the return value (slug), its role with other tools, and the de-duplication behavior. No output schema exists, so the description carries that burden, and it does so well. Nothing an agent needs to call it correctly 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 coverage is 100% (body, title), so baseline is 3. The description adds value by explaining the relationship: slug derived from title and de-duplicated automatically. This clarifies the title parameter's role beyond its schema description. Body is straightforward.
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 the action (create) on the resource (markdown note) and immediately explains the key return value (slug). It distinguishes this tool from siblings (update, delete, search, read) by focusing on creation and the slug as the identifier for other tools.
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 is the creation tool and explains the slug's role as the identifier that other tools use (read, update, delete). It doesn't explicitly list alternatives or exclusion criteria, but the purpose is unmistakable from the name and description, so this is firmly a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Permanently delete a note by slug. This cannot be undone.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug of the note to delete. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already include destructiveHint=true and readOnlyHint=false, and the description adds meaningful behavioral context with 'Permanently delete' and 'This cannot be undone.' It does not contradict the annotations, though it could go further by noting behavior for nonexistent slugs or permission requirements.
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 with no filler. The key fact—permanent deletion—is front-loaded, and irreversibility is stated immediately. Every word 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 destructive operation with annotations already signaling destructive and read-only characteristics, the description is complete enough. The agent knows what happens, what identifier is required, and the irreversible consequence.
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 parameter slug is already described in the schema. The description only restates 'by slug' without adding additional meaning or format details, so it stays at the baseline for a fully documented schema.
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 ('delete'), a specific resource ('note'), and the exact key ('by slug'). This clearly distinguishes it from siblings like create_note, read_note, update_note, and list_notes.
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?
No guidance is given about when to use this tool versus alternatives, such as update_note for modifying a note or search_notes for finding one. The description only implies destruction but provides no exclusions, prerequisites, or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List note summaries (no bodies), most recently updated first. Use this to get your bearings before reading or searching.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Only return notes carrying this tag. | |
| limit | No | Max notes to return. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true already covering the safety profile, the description adds value by disclosing that responses contain only summaries (no bodies) and are sorted by recency. This is concrete behavioral context beyond the annotation; it does not describe pagination or the contents of a summary, which keeps it from a 5.
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 redundancy: the first packs the operation, output scope, and sort order; the second adds a single piece of usage guidance. Every clause earns its place, and key facts are front-loaded.
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 two-optional-parameter list tool with a read-only annotation, the description covers the core invocation needs: what is returned (summaries), what is excluded (bodies), and ordering. The only gap is that 'summaries' is not defined (e.g., which fields appear), and there is no output schema to compensate, so it is not a 5.
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%: both 'tag' and 'limit' have clear inline descriptions, so the schema carries the parameter documentation. The tool description itself adds no parameter-level meaning, matching the baseline of 3 for high coverage.
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 clear verb ('list'), resource ('notes'), and immediately scopes the result to 'summaries (no bodies)', which differentiates it from read_note and search_notes. It also pins the ordering to 'most recently updated first', leaving no ambiguity about what the tool does.
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?
'Use this to get your bearings before reading or searching' explicitly frames when to call it, positioning it as the orientation step ahead of content retrieval and search. It implies but does not explicitly name the sibling alternatives (read_note for full bodies, search_notes for search), so it stops short of a full when-not/alternatives breakdown.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read a single note in full, by slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Slug of the note, e.g. 'pad-thai'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description doesn't need to state that it's a read operation. The description adds that it reads 'in full' and uses a slug, which is useful but minimal. No side effects or limitations are mentioned, but the read-only nature is already covered.
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, front-loaded sentence conveys exactly what the tool does without any filler. Every word 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 read operation with one parameter and readOnlyHint annotation, this is nearly complete. The description tells the agent the key input and outcome. Lack of an output schema is acceptable because 'in full' implies the entire note. Minor gaps like error handling or not-found behavior are not critical for a straightforward read.
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% with a clear description of the slug parameter. The description merely reiterates 'by slug', adding no new semantic detail beyond the schema. Baseline 3 is appropriate since the schema does the heavy lifting.
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 the verb (read), the resource (a single note), and the scope (in full, by slug). It distinguishes itself from siblings like list_notes (multiple notes) and search_notes (query-based) by specifying 'single note' and the unique identifier 'slug'.
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 context: use this tool when you have a slug and want the entire note. However, it does not explicitly mention when not to use it (e.g., for listing or searching) or name alternatives, so it lacks exclusions. Still, the intended use is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-text search over titles, tags, and bodies. Returns ranked hits with a snippet; matches in the title or tags outrank matches in the body.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max hits to return. | |
| query | Yes | Search terms, whitespace separated. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation readOnlyHint already indicates read-only behavior. The description adds valuable details about return format (ranked hits with snippets) and ranking rules (title/tag matches outrank body matches), enhancing transparency beyond the annotation.
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 concise sentences with no redundancy. Each phrase contributes meaning (search scope, return type, ranking rule), achieving high information density without clutter.
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?
Combined with the schema and annotation, the description covers purpose, return behavior, and ranking logic. An agent has sufficient information to invoke the tool correctly, including expected output format and ranking priority.
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 input schema already provides descriptions for both parameters ('query' and 'limit'). The tool description does not add parameter-specific semantics beyond what is already documented, so it only meets the baseline for a fully covered schema.
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 ('search'), resource ('notes'), and scope ('over titles, tags, and bodies'). It distinguishes from sibling tools like list_notes by emphasizing full-text search and ranking, making the purpose unambiguous.
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 when to use the tool (for content-based search) but does not explicitly contrast with alternatives (e.g., 'use list_notes to retrieve all notes'). It is clear enough for an agent to infer the appropriate scenario, though explicit guidance would be stronger.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Replace the title, body, and/or tags of an existing note. Omitted fields are left untouched. The slug never changes.
| Name | Required | Description | Default |
|---|---|---|---|
| body | No | New markdown body, replacing the old one. | |
| slug | Yes | Slug of the note to update. | |
| tags | No | New tag list, replacing the old one. | |
| title | No | New title. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the operation is destructive and not read-only, and the description reinforces this by saying fields are 'replaced.' It adds valuable behavioral detail beyond the annotations: omitted fields are left untouched and the slug never changes, which clarifies partial-update semantics and identity stability.
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, no filler. The first sentence states the operation and scope, the second clarifies the partial-update behavior, and the third removes a common concern about identifier changes. 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 4-parameter mutation tool with full schema coverage and no output schema, the description covers the essential behavioral contract: what can be changed, what is preserved, and what remains stable. Nothing critical is missing for an agent to 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 coverage is 100%, so the baseline is 3, but the description adds meaning by grouping 'title, body, and/or tags' as optional replacement fields and specifying that the slug is immutable. This helps an agent understand that omitting a field means preserving its current value, which the schema alone does not convey.
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 ('Replace') with a clear resource ('an existing note') and names the exact fields that change: title, body, and/or tags. It is instantly distinguishable from the sibling tools create_note, read_note, list_notes, search_notes, and delete_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?
The description clearly situates the tool as a mutation of an existing note, which implicitly excludes creation, reading, listing, searching, and deletion. It does not explicitly name alternatives or state when not to use it, but the context is clear enough for agent selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0create_notedelete_notelist_notesread_notesearch_notesupdate_noteScored across 6 tools
Each tool has a distinct purpose: create, read, list, search, update, and delete notes. No two tools overlap in functionality, and the descriptions clearly differentiate them.
All tool names follow a consistent verb_noun pattern (create_note, read_note, list_notes, search_notes, update_note, delete_note), making the API predictable and easy to navigate.
With 6 tools covering the full CRUD lifecycle plus listing and search, the set is well-scoped for a notes server. No redundant or missing tools; each earns its place.
The tool surface provides complete coverage for note management: create, read, list, search, update, and delete. There are no obvious dead ends or missing operations for the intended domain.
Markdown notes and whiteboards your AI agent can read and write.
Markdown notes in folders, with files, that your AI assistant can read, write and organise.
Markdown notes in folders, with files, that your AI assistant can read, write and organise.
Search, read, and safely update Markdown notes in your connected Phasoric knowledge vaults.
