OMOS-MCP
OMOS-MCP
MCP-Server, der KI-Agenten (Claude, ChatGPT, Cursor usw.) das Lesen von Projektdokumenten im OMOS Shared Drive ermöglicht, um Fragen zu beantworten – jede Antwort verweist auf die Quelldatei mit einem Google Drive-Link.
Unterstützte Drive-Struktur:
OMOS/
├── Project A/
│ ├── Project Overview/
│ ├── Timeline/
│ ├── Design/
│ │ ├── System Flow/
│ │ ├── DB/
│ │ └── API/
│ └── BRD/
└── Project B/ ...Tools
Tool | Beschreibung |
| Indexiert den gesamten Drive: alle Projekte + alle Dateien mit ID und Link (der Agent ruft dies immer zuerst auf). |
| Volltextsuche im gesamten Drive, eingrenzbar auf Projekt/Abschnitt. |
| Liest eine Datei als Text – Google Docs/Sheets/Slides, PDF, .docx, .xlsx, md/text und Bilder (sendet das Bild direkt an den Agenten). |
| Baut den Index sofort neu auf (Standard-Cache 5 Minuten). |
Der Agent durchsucht in dieser Reihenfolge: Filter Projektname → nicht gefunden → Projektübersicht anzeigen und beim Benutzer bestätigen lassen → weiterfragen nach Zeitplan | BRD | Design (Systemablauf / DB / API) → zu breite Fragen werden zurückgewiesen, um den Umfang einzugrenzen (in den Server-Anweisungen hinterlegt).
Related MCP server: brkt-gdrive-mcp
Einmalige Einrichtung: Service Account
Der Server greift über einen Service Account auf den Drive zu (kein Login für alle erforderlich):
Gehe zu console.cloud.google.com → neues Projekt erstellen (oder ein vorhandenes verwenden).
APIs & Services → Library → nach Google Drive API suchen → Enable klicken.
IAM & Admin → Service Accounts → Create Service Account → Namen vergeben, z. B.
omos-mcp→ Create klicken (Rollen-Schritt überspringen, keine Berechtigungen erforderlich).Zum erstellten Service Account gehen → Tab Keys → Add Key → Create new key → JSON → die Schlüsseldatei wird heruntergeladen.
Die E-Mail des Service Accounts kopieren (sieht aus wie
omos-mcp@<project>.iam.gserviceaccount.com) und den Shared Drive / OMOS-Ordner für diese E-Mail als Viewer freigeben.Die Ordner-ID des OMOS-Root-Ordners finden: Ordner im Browser öffnen und die URL betrachten
https://drive.google.com/drive/folders/<dies ist die id>.
Environment Variables
Variable | Beschreibung |
| Ordner-ID des OMOS-Root-Ordners (aus Schritt 6) – erforderlich |
| Inhalt der gesamten JSON-Schlüsseldatei oder Pfad zur Datei – erforderlich |
| (HTTP-Modus) Bearer-Token, den der Client senden muss |
| Cache-Lebensdauer des Index in Sekunden (Standard: 300) |
| (HTTP-Modus) OAuth-Modus für Claude.ai / ChatGPT Web aktivieren |
| (HTTP-Modus) Port, auf dem gelauscht wird (Standard: 8000) |
Lokal ausführen
Zuerst uv installieren (curl -LsSf https://astral.sh/uv/install.sh | sh).
1) .env einrichten (der Server lädt automatisch aus dem Repository-Stammverzeichnis):
cp .env.example .env.env bearbeiten, OMOS_ROOT_FOLDER_ID und den Pfad zur Schlüsseldatei eintragen (z. B. die Schlüsseldatei unter ./service-account.json ablegen – ist bereits in .gitignore).
2) Mit Claude Code verbinden (stdio):
claude mcp add omos -- uv run --directory "/path/to/OMOS-MCP" omos-mcpClaude Desktop / Cursor / Windsurf – in der MCP-Konfiguration eintragen:
{
"mcpServers": {
"omos": {
"command": "uv",
"args": ["run", "--directory", "/path/to/OMOS-MCP", "omos-mcp"]
}
}
}3) Testen: Den Agenten fragen "Welche Projekte gibt es in OMOS?" – es sollte eine Liste der Projekte mit Links erscheinen.
(Optional) Als HTTP-Server lokal ausführen: OMOS_AUTH_TOKEN in .env aktivieren, dann
uv run omos-mcp-httpDer Endpunkt ist http://localhost:8000/mcp. Verbinden mit:
claude mcp add --transport http omos http://localhost:8000/mcp -H "Authorization: Bearer <OMOS_AUTH_TOKEN>"Als MCP-Link deployen (Render)
Dieses Repository auf GitHub pushen (enthält bereits
Dockerfile+render.yaml).Render → New → Blueprint → dieses Repository auswählen.
Umgebungsvariablen im Dashboard setzen:
OMOS_ROOT_FOLDER_ID= Ordner-ID des OMOS-Root-OrdnersGOOGLE_SERVICE_ACCOUNT_JSON= Inhalt der gesamten JSON-Schlüsseldatei (direkt einfügen)OMOS_AUTH_TOKENRender generiert selbst → kopieren und an das Team weitergeben
Nach erfolgreichem Deployment ist der Endpunkt
https://<app>.onrender.com/mcp.
Benutzer verbinden sich:
claude mcp add --transport http omos https://<app>.onrender.com/mcp \
-H "Authorization: Bearer <OMOS_AUTH_TOKEN>"Cursor / VS Code: URL + Header Authorization: Bearer <token> in der MCP-Konfiguration eintragen.
Für Claude.ai / ChatGPT (Web) – OAuth
Web-Clients haben kein Feld für Header, daher muss OAuth über einen Anbieter wie WorkOS AuthKit (kostenlos für bis zu 1 Mio. Benutzer) verwendet werden:
Bei workos.com registrieren → AuthKit aktivieren → Login-Methode einrichten + auf Teammitglieder beschränken.
Dashboard → Connect → Configuration → Client ID Metadata Document und Dynamic Client Registration aktivieren.
Resource Indicator hinzufügen =
https://<app>.onrender.com(ohne/mcp).Auf Render 3 zusätzliche Umgebungsvariablen setzen:
OAUTH_ISSUER= AuthKit-Domain,OAUTH_AUDIENCE=https://<app>.onrender.com,PUBLIC_URL=https://<app>.onrender.com.Benutzer: Claude.ai → Settings → Connectors → Add custom connector →
https://<app>.onrender.com/mcpeingeben → über WorkOS anmelden.
OAuth-Modus und Bearer-Token können nebeneinander existieren – Web verwendet OAuth, CLI kann weiterhin das Token verwenden.
Konverter testen
uv run python test_convert.pyAvailable Tools
4 toolsomos_indexA
Get the full index of the OMOS drive: every project and its files (with file ids and links). Use this whenever the user asks about any internal project, BRD, timeline, design, system flow, DB, API, or เอกสารโปรเจค — ALWAYS call this first, then filter by Project Name before drilling into Timeline / BRD / Design (System Flow, DB, API).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It explicitly states the tool returns the full index of every project with file IDs and links, and implies a read-only operation (no side effects). It does not mention performance, caching, or rate limits, but the zero-parameter nature and output schema presence mitigate the need for deeper behavioral detail. The description adds meaningful context beyond the empty schema and missing 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 concise at three sentences with no wasted words. The first sentence clearly states the core purpose and what is returned. The second sentence provides specific when-to-use guidance. The third sentence prescribes the correct invocation sequence. Every sentence adds distinct value and the critical usage instructions 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?
Given the tool has zero parameters and an output schema, the description covers all necessary aspects: what the tool returns (full index of projects and files with IDs and links), when to use it (specific user query scenarios), and how to use it correctly (call first, then filter by project name before drilling into details). The presence of an output schema reduces the need to describe return values, and the description's guidance is fully self-contained.
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 has zero parameters and 100% schema coverage, meaning there is nothing for the description to add about parameters. The description correctly stays silent on parameters (there are none) and instead focuses on what the tool returns and when to use it. A slight deduction because the description could explicitly state 'no arguments required' to confirm no parameters are needed.
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 starts with a clear verb+resource combination: 'Get the full index of the OMOS drive' and immediately specifies the exact content: every project and its files with IDs and links. It thoroughly distinguishes itself from siblings like 'omos_search' by stating it returns the full unfiltered index, while also providing specific use cases where this tool should be used first.
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 explicit when-to-use guidance: 'whenever the user asks about any internal project, BRD, timeline, design, system flow, DB, API, or เอกสารโปรเจค.' It also provides a sequence: 'ALWAYS call this first, then filter by Project Name before drilling into Timeline / BRD / Design.' This effectively differentiates from omos_search (which presumably handles the filtering step) and sets clear expectations for invocation order.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
omos_readA
Read a file from the OMOS drive as text (Google Docs/Sheets/Slides, PDF, docx, xlsx, markdown/text) or as an image (png/jpg). The response starts with a citation block (file name, path, link) — always include that citation in your answer.
Args: file_id: the Drive file id from omos_index or omos_search
| Name | Required | Description | Default |
|---|---|---|---|
| file_id | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behaviors: it returns text or image content, starts with a citation block that must be included in answers, and accepts a Drive file ID from omos_index or omos_search. Since no annotations are provided, the description carries the full burden and provides solid behavioral context without contradictions.
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 short and well-structured: a one-sentence overview of what the tool does (including format types), then a specific instruction about the citation block, and finally a clear parameter description. Every sentence adds value, and the most critical information is 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?
Given the tool has only one parameter, limited schema, and no output schema, the description is complete for its simplicity. It explains the input, output format (including citation), and supported file types. A small gap is not explaining how the output type (text vs image) is determined (e.g., by file extension or a parameter).
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 description explains that the file_id parameter should come from omos_index or omos_search, adding meaning beyond the schema's simple type/required. With 0% schema description coverage, the description must compensate, and it does so effectively by specifying the source of valid file IDs.
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 tool reads a file from the OMOS drive and specifies supported formats (text types and image types). The verb 'read' combined with the resource 'file from OMOS drive' provides a specific, actionable purpose that distinguishes it from sibling tools like omos_index (indexing) and omos_search (searching).
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 for when to use this tool (to read a file), and implicitly distinguishes it from omos_index and omos_search which are for listing or searching. However, it does not explicitly state when NOT to use it or mention alternatives for non-file operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
omos_refreshA
Rebuild the drive index so newly added projects/files show up immediately (the index is otherwise cached for a few minutes).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
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 caching side effect and the rebuild action, but does not mention potential costs (e.g., performance impact), whether it is asynchronous, or any required permissions. The behavior is generally clear but lacks some depth expected for an unannotated tool.
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, well-structured sentence that immediately conveys the action and purpose. No redundant words; 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 simple parameterless tool with an output schema, the description covers the core functionality and caching behavior. It could be improved by noting typical usage scenarios (e.g., after uploads) or safe call frequency, but it is largely complete.
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?
There are no parameters; schema description coverage is 100% by default. The description does not need to add parameter details. 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 explicitly states the action ('Rebuild the drive index') and the effect ('so newly added projects/files show up immediately'). It distinguishes itself from sibling tools (index, search, read) by focusing on cache invalidation.
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 provides clear context on when to use the tool—after adding projects/files to bypass caching. It mentions the default caching behavior ('otherwise cached for a few minutes'), but does not explicitly state when not to use it or list alternative tools for similar purposes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
omos_searchA
Full-text search across the OMOS drive. Returns matching files with project path and link; use omos_read on the ids to read content.
Args: query: keyword or phrase (searches file content and names) project: optional exact project name from omos_index to narrow the search section: optional section name (e.g. 'BRD', 'Timeline', 'Design', 'DB') to narrow further
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| project | No | ||
| section | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It accurately describes the search behavior, confirms it returns files with path and link, and mentions it searches both content and names. Could mention pagination or limits but not required for basic 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 paragraphs with minimal waste; first sentence immediately states purpose, then parameter list is clean. Every sentence adds value and is 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?
Given output schema exists, description need not detail return values; it still says 'Returns matching files with project path and link'. Parameter descriptions are thorough, and sibling references (omos_read, omos_index) provide complete context for usage.
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 0%, but description fully compensates by explaining each parameter's purpose: query searches content/names, project references omos_index for exact name, section gives concrete examples (BRD, Timeline, etc.). This adds significant meaning beyond the bare 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?
Clearly states verb 'search' and resource 'OMOS drive', specifies full-text search, and explicitly directs to use omos_read for content retrieval, effectively distinguishing from siblings.
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?
Provides explicit guidance to use omos_read after search and mentions omos_index for project names, though it does not explicitly state when not to use this tool or describe alternatives beyond omos_read.
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
omos_index - First observed
omos_read - First observed
omos_refresh - First observed
omos_search
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: omos_index provides the full index, omos_search performs full-text search with optional filters, omos_refresh rebuilds the index, and omos_read reads file content. There is no overlap or ambiguity between them.
All tools follow a consistent 'omos_<verb>' pattern (index, search, refresh, read). The naming is predictable and uniform, making it easy for an agent to understand the action each tool performs.
With 4 tools, the set is well-scoped for a drive retrieval server. Each tool addresses a necessary function (listing, searching, refreshing, reading) without being too few or excessive. The count matches the domain's core operations.
The tool surface covers the full read lifecycle for the OMOS drive: obtaining the index, searching, refreshing for updates, and reading file content. No obvious gaps exist for a retrieval-focused server; all necessary operations are present.
Maintenance
Related MCP Connectors
MCP server connecting AI agents to 100+ apps (Gmail, Slack, Notion, GitHub) via one-click OAuth.
MCP server for generating rough-draft project plans from natural-language prompts.
Read-only MCP server exposing a user ORANO library to their own AI agent.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceAn MCP server that enables users to query documents from Google Drive using AI assistance, providing tools for searching vector databases and generating answers based on document content.-
- FlicenseNot gradedqualityBmaintenanceMCP server for semantic search over Google Drive documents, enabling AI tools to search, list, and retrieve document content.-
- FlicenseNot gradedqualityDmaintenanceAn MCP server that enables AI assistants to interact with Google Docs, Sheets, and Drive using the user's own Google account.-
- AlicenseNot gradedqualityDmaintenanceMCP server that reads Google Docs and Google Sheets content for AI assistants like Claude.8 npm1MIT