Skip to main content
Glama

lore

License: MIT TypeScript lore MCP server

Semantische Suche in Ihren Claude Code- und OpenAI Codex CLI-Konversationen. Finden Sie alles, was Sie jemals besprochen haben -- über alle Projekte, alle Sitzungen, jeden Branch und jeden Agenten hinweg.

lore MCP server

Funktionen

  • Hybride Suche (Vektor + Stichwort) Kombiniert multilingual-e5-small Embeddings mit FTS5/BM25 mittels Reciprocal Rank Fusion. Findet Ergebnisse sowohl nach Bedeutung als auch nach exakten Begriffen.

  • Multi-Agent: Claude Code + Codex CLI Indiziert sowohl ~/.claude/projects/ (Claude Code) als auch ~/.codex/sessions/ (OpenAI Codex CLI) in derselben Datenbank. Codex-Sitzungen werden nach cwd aus session_meta gruppiert und als virtuelle codex-<path>-Projekte angezeigt, sodass Sie diese gemeinsam durchsuchen oder auf einen Agenten filtern können.

  • Vollständig lokal, keine API-Schlüssel Alles läuft auf Ihrem Rechner. ONNX Runtime für Embeddings, sqlite-vec für die Speicherung. Keine Daten verlassen Ihr Gerät.

  • Automatische Indizierung bei Sitzungsende Ein SessionEnd-Hook indiziert automatisch alle neuen Sitzungen im Hintergrund. Keine manuellen Auslöser erforderlich.

  • Hintergrundindizierung Manuelle Indizierungsauslöser kehren sofort zurück. Überwachen Sie den Fortschritt, während Sie weiterarbeiten. Durchsuchen Sie bereits indizierte Inhalte, während der Rest verarbeitet wird.

  • Standardmäßig Opt-out Alle Projekte werden automatisch indiziert. Schließen Sie die Projekte aus, die Sie nicht benötigen. Keine Registrierung erforderlich.

  • Konversationsbewusstes Chunking Unterteilt nach logischen Abschnitten (Benutzerfrage + vollständige Antwortkette des Assistenten), nicht nach willkürlichen Token-Fenstern. Verarbeitet Tool-Use-Ketten, Denkblöcke und mehrstufige Interaktionen korrekt.

  • 100+ Sprachen Koreanisch, Japanisch, Chinesisch, Englisch und über 90 weitere. CJK-bewusste Token-Schätzung für präzises Chunking.

Related MCP server: Semantic Search MCP Server

Schnellstart

Zu Claude Code hinzufügen

# No install needed — always runs latest version
claude mcp add -s user lore -- npx getlore

# Or for a single project only
claude mcp add -s project lore -- npx getlore

Zu OpenAI Codex CLI hinzufügen

# No install needed
codex mcp add lore -- npx getlore
npm install -g getlore

# Then register with your tool:
claude mcp add -s user lore -- getlore   # Claude Code
codex mcp add lore -- getlore            # Codex CLI

# Manage your install:
getlore --version   # Check installed version
getlore update      # Update to latest

Verwendung

Sobald die Verbindung hergestellt ist, kann die KI die Tools von lore direkt nutzen:

You: "What did we discuss about auth refactoring last week?"

Claude: [calls lore search] Found 3 relevant conversations...
        In your "my-webapp" project on March 15, you decided to...

Ersteinrichtung:

  1. Index -- index() scannt automatisch alle Projekte, läuft im Hintergrund

  2. Suche -- Fragen Sie alles über vergangene Konversationen

  3. Ausschließen (optional) -- Verstecken Sie Projekte, die Sie nicht interessieren

Tools

Tool

Zweck

manage_projects

Projekte von der Indizierung ausschließen/einschließen (Opt-out-Modell)

index

Hintergrundindizierung starten. Alle nicht ausgeschlossenen Projekte. Modi: incremental (Standard), rebuild, cancel

status

Indizierungsfortschritt, ETA, Gründe für das Überspringen, DB-Status prüfen

search

Semantische + Stichwortsuche über Konversationen hinweg

get_context

Suchergebnisse mit der umgebenden Konversation erweitern

list_sessions

Indizierte Sitzungen nach Projekt durchsuchen

Warum existiert dieses Tool?

Claude Code speichert jede Konversation als JSONL-Transkript in ~/.claude/projects/, und OpenAI Codex CLI speichert seine Rollouts in ~/.codex/sessions/YYYY/MM/DD/. Nach ein paar Wochen haben Sie Hunderte von Sitzungen in Dutzenden von Projekten, die oft auf beide Agenten verteilt sind -- Diskussionen über Architektur-Entscheidungen, Debugging-Sitzungen, Code-Reviews und Design-Explorationen.

Es gibt jedoch keine Möglichkeit, diese zu durchsuchen. Sie können nicht fragen: "Welchen Ansatz haben wir für die Auth-Middleware gewählt?" oder "In welchem Projekt gab es diese Diskussion über die Datenbankmigration?"

Bestehende Tools erfordern entweder Cloud-APIs, erzeugen Zombie-Prozesse oder behandeln Konversationen als generische Dokumente. lore wurde speziell für KI-Coding-Sitzungen entwickelt: Es versteht Gesprächsgrenzen, Tool-Use-Ketten und Denkblöcke und parst sowohl Claude Code- als auch Codex-JSONL-Formate nativ. Es läuft vollständig lokal ohne Abhängigkeiten außer Node.js.

Funktionsweise

~/.claude/projects/*/*.jsonl     ~/.codex/sessions/YYYY/MM/DD/rollout-*.jsonl
        \                                       /
         \                                     /
          JSONL Parser (Claude Code + Codex formats, skips noise)
                              |
          Turn-pair Chunker (groups by logical conversation turns)
                              |
          Transformers.js (multilingual-e5-small, INT8 quantized, 384d)
                              |
          sqlite-vec + FTS5 (hybrid vector + keyword storage)
                              |
          Reciprocal Rank Fusion (combines both signals for ranking)

Codex-Sitzungen werden nach cwd gruppiert, das aus der session_meta-Zeile jeder Datei extrahiert wird, und als virtuelle codex-<path>-Projekte im Index angezeigt.

Speicherung: Einzelne SQLite-Datei unter ~/.lore/lore.db mit WAL-Modus für gleichzeitige Lesezugriffe.

Konfiguration: Projektausschlüsse werden in ~/.lore/config.json gespeichert.

Umgebungsvariablen

Variable

Standard

Beschreibung

LORE_DIR

~/.lore

Datenverzeichnis

LORE_DB

~/.lore/lore.db

Datenbankpfad

CLAUDE_PROJECTS_DIR

~/.claude/projects

Speicherort der Claude Code-Transkripte

CODEX_SESSIONS_DIR

~/.codex/sessions

Speicherort der OpenAI Codex CLI-Rollouts

Gemessen auf Apple Silicon (M-Serie):

Metrik

Wert

Suchlatenz

20-30ms

Indizierungsgeschwindigkeit

~10 Sitzungen/Sek.

Erste Suche (kaltes Laden des Modells)

~5s

DB-Größe

~0,1MB pro 10 Sitzungen

Modellgröße (einmalig heruntergeladen)

~112MB

"Keine Sitzungen gefunden"

Führen Sie manage_projects mit der Aktion list aus, um verfügbare Projekte anzuzeigen. Alle werden standardmäßig indiziert, sofern sie nicht ausgeschlossen wurden.

Veraltete Sperrdatei

Wenn die Indizierung unterbrochen wurde, wird die Sperrdatei beim nächsten Lauf automatisch bereinigt (PID-basierte Erkennung).

DB-Beschädigung

Löschen Sie ~/.lore/lore.db und indizieren Sie neu. Ihre Quelldaten (~/.claude/projects/) werden niemals verändert.

Entwicklung

git clone https://github.com/hyunjae-labs/lore.git
cd lore
npm install
npm run build
npm test          # 135 tests

Tech-Stack

Lizenz

MIT

Available Tools

6 tools
get_contextA

Retrieve more conversation context around a specific search result. Use ONLY after calling search, when you need to see what was discussed before or after a result.

ParametersJSON Schema
NameRequiredDescriptionDefault
chunk_idYes
directionNo
countNo

TDQS

A3.8/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool retrieves context, it lacks details on permissions, rate limits, error handling, or what the output looks like (e.g., format, size limits). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 highly concise and front-loaded, with two sentences that directly state the purpose and usage guidelines without any wasted words. Every sentence earns its place by providing essential information efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description covers purpose and usage well but is incomplete. It lacks details on parameters, behavioral traits, and output format, which are necessary for full understanding. The description is adequate as a minimum but has clear gaps in context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It only vaguely references 'a specific search result' (implied to relate to 'chunk_id') and 'before or after a result' (implied to relate to 'direction'), but provides no specifics on parameter meanings, formats, or constraints. This fails to adequately explain the three parameters beyond basic schema hints.

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 clearly states the specific action ('Retrieve more conversation context') and resource ('around a specific search result'), distinguishing it from siblings like 'search' (which finds results) or 'list_sessions' (which lists sessions). It explicitly defines the tool's scope as fetching contextual conversation snippets.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool ('Use ONLY after calling search, when you need to see what was discussed before or after a result'), including a prerequisite (must call 'search' first) and a clear use-case (viewing surrounding context). It effectively differentiates from alternatives by specifying its post-search role.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

indexA

Update the search index with recent Claude Code sessions. Call if search returns stale results or the user asks to refresh the index. Modes: 'incremental' (default, only new/changed), 'full' (delete all and rebuild from scratch), 'cancel' (stop running index).

ParametersJSON Schema
NameRequiredDescriptionDefault
modeNo
projectNo
confirmNo

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by explaining the three modes and their behaviors ('incremental' for new/changed, 'full' for delete and rebuild, 'cancel' to stop). It could mention performance impact or permissions but covers core operational traits.

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?

Three sentences with zero waste: first states purpose, second gives usage guidelines, third details modes. Each sentence earns its place, and the structure is front-loaded with essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with 3 parameters, no annotations, and no output schema, the description is quite complete—covering purpose, usage, and key parameter semantics. It could note that 'full' mode might be resource-intensive or that 'confirm' is for safety, but it's largely adequate.

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 description coverage is 0%, so the description must compensate. It explains the 'mode' parameter's three values and their meanings, which adds crucial semantics beyond the bare enum in the schema. It doesn't cover 'project' or 'confirm', but the mode explanation is substantial.

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 clearly states the tool's purpose with specific verbs ('Update the search index') and resources ('recent Claude Code sessions'), distinguishing it from sibling tools like 'search' or 'list_sessions' which query rather than update the index.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is provided on when to use this tool: 'if search returns stale results or the user asks to refresh the index.' This directly addresses the tool's purpose relative to alternatives like 'search'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_sessionsB

List all indexed Claude Code sessions. Use when the user wants to browse conversation history or find sessions by project/date.

ParametersJSON Schema
NameRequiredDescriptionDefault
projectNo
limitNo
sortNo

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that sessions are 'indexed' and implies filtering capabilities ('by project/date'), but lacks details on permissions, rate limits, pagination, or what 'indexed' entails. For a list tool with zero annotation coverage, this leaves significant gaps in understanding the tool's behavior and constraints.

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 concise and well-structured, consisting of two sentences that efficiently convey the tool's purpose and usage. The first sentence states what it does, and the second provides context for when to use it, with no wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral aspects like permissions or rate limits, and parameter semantics are underspecified. Without an output schema, it also doesn't describe return values (e.g., session format). For a tool with moderate complexity and no structured support, the description should provide more comprehensive guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 3 parameters with 0% description coverage, meaning no parameter details are documented in the schema. The description only vaguely references 'project/date' for filtering, which partially covers the 'project' parameter but ignores 'limit' and 'sort'. It doesn't explain what 'limit' controls (e.g., number of results) or the meaning of 'sort' enum values ('recent', 'oldest'), failing to compensate for the low schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'List all indexed Claude Code sessions.' It specifies the verb ('List') and resource ('indexed Claude Code sessions'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate this tool from sibling tools like 'search' or 'get_context', which might also involve session retrieval.

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 description provides clear usage guidance: 'Use when the user wants to browse conversation history or find sessions by project/date.' This gives context for when to invoke the tool, such as for browsing or filtering by project/date. It doesn't explicitly state when not to use it or name alternatives like 'search', but the context is sufficient for basic decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

manage_projectsA

Manage which projects are registered for indexing. Use 'list' to see all projects on disk and their registration status. Use 'add' to register a project for indexing. Use 'remove' to unregister. Projects must be registered before they can be indexed.

ParametersJSON Schema
NameRequiredDescriptionDefault
actionYes
projectNo

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the three discrete actions and the registration requirement, but doesn't mention permissions needed, whether changes are reversible, rate limits, or what the response looks like. For a mutation tool with zero annotation coverage, this leaves significant gaps.

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 efficiently structured with three sentences: an overview statement, specific action explanations, and a prerequisite. Every sentence adds value with no redundant information, making it easy to parse and understand.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 2-parameter tool with no annotations and no output schema, the description provides good purpose and usage guidance but lacks details about response format, error conditions, and the exact format of the 'project' parameter. It's adequate but has clear gaps in behavioral transparency.

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?

With 0% schema description coverage, the description must compensate. It explains the meaning of the 'action' parameter values ('list', 'add', 'remove') and implies the 'project' parameter is used with 'add' and 'remove' actions. However, it doesn't specify what format the 'project' parameter expects (path, name, ID).

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 clearly states the tool's purpose with specific verbs ('manage', 'list', 'add', 'remove') and resources ('projects', 'indexing'), distinguishing it from sibling tools like 'index' or 'search'. It explains that this tool handles registration status for indexing, not the indexing process itself.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use each action ('list' to see status, 'add' to register, 'remove' to unregister) and includes a prerequisite statement ('Projects must be registered before they can be indexed') that helps differentiate from the 'index' sibling tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

statusA

Check the health and progress of lore indexing. Shows indexing status, session counts, DB size. Use this to monitor indexing progress after calling index.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes what the tool does (checking health/progress and showing specific metrics) but lacks details on permissions needed, rate limits, or what happens if indexing isn't running. It doesn't contradict annotations, but could be more informative.

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 appropriately sized with two sentences that are front-loaded: the first states the purpose and what it shows, the second provides usage guidance. Every sentence adds value without redundancy or waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is reasonably complete. It explains the tool's purpose, what it returns, and when to use it. However, without an output schema, it could benefit from more detail on return format or error conditions, but this is minor for a status-check tool.

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 0 parameters with 100% schema description coverage, so the schema fully documents the lack of inputs. The description doesn't need to add parameter information, but it implicitly confirms no parameters are needed by not mentioning any. This meets the baseline for zero-parameter tools.

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 clearly states the tool's purpose with specific verbs ('check', 'shows') and resources ('health and progress of lore indexing', 'indexing status, session counts, DB size'). It distinguishes from siblings by focusing on monitoring rather than performing operations like 'index' or 'search'.

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 description provides clear context for usage ('Use this to monitor indexing progress after calling index'), indicating when to use it in relation to the 'index' sibling tool. However, it doesn't explicitly state when not to use it or mention alternatives among other siblings like 'list_sessions' or 'get_context'.

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. Dates show when Glama detected each change.

  1. 6 tool updates
    • First observedget_context
    • First observedindex
    • First observedlist_sessions
    • First observedmanage_projects
    • First observedsearch
    • First observedstatus

TDQS

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: get_context retrieves conversation context around search results, index updates the search index, list_sessions lists indexed sessions, manage_projects handles project registration, search performs searches, and status checks indexing health. The descriptions explicitly differentiate their use cases, preventing agent confusion.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern and use clear verbs like get, index, list, manage, search, and status. However, 'status' deviates slightly as a noun rather than a verb (e.g., 'check_status' would be more consistent), but overall the naming is predictable and readable.

Tool Count5/5

With 6 tools, this server is well-scoped for its purpose of managing and searching conversation history. Each tool serves a specific function in the indexing and retrieval workflow, from setup (manage_projects, index) to query (search, get_context) and monitoring (list_sessions, status), with no unnecessary bloat.

Completeness5/5

The tool set provides complete coverage for the domain of indexing and searching Claude Code sessions. It includes project management (manage_projects), indexing operations (index, status), session listing (list_sessions), search functionality (search), and context retrieval (get_context), ensuring agents can handle the full lifecycle without gaps.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

Related MCP Servers

  • -
    license
    Not graded
    quality
    Not graded
    maintenance
    Enables comprehensive search and analysis of Claude Code conversation history using full-text search, optional semantic vector search, and conversation management tools. Provides fast SQLite-based indexing with role-based filtering, project organization, and hybrid search capabilities combining keyword and semantic matching.
    -
  • F
    license
    Not graded
    quality
    B
    maintenance
    Provides hybrid semantic and keyword code search for Claude Code using BM25 and vector retrieval. It enables indexing and searching local codebases with language-aware chunking and local embeddings.
    -
  • A
    license
    A
    quality
    A
    maintenance
    Enables local semantic search over documents and code for Claude Code and Claude Desktop, running entirely offline with local embeddings and vector storage.
    12
    3
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Provides Claude Code with local semantic search and indexing of your codebase using AST-aware chunking and hybrid search, enabling deep code understanding without sending data to the cloud.
    MIT

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/hyunjae-labs/lore'

If you have feedback or need assistance with the MCP directory API, please join our Discord server