PyAirbyte MCP Server (quintonwall/airbyte-labs-pyairbyte-mcp) is an MCP server listed on the M8ven Trust Index. It scores 71 out of 100, grade C. It declares 1 tool. No publisher has claimed this listing.

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C
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71/100

PyAirbyte MCP Server

Generates complete PyAirbyte data pipeline code and setup instructions for moving data between 600+ Airbyte source and destination connectors or to Pandas DataFrames, using AI-powered context-aware guidance based on connector documentation.

→ quintonwall/airbyte-labs-pyairbyte-mcp· listed on glama

Limited view. Automated analysis covers part of this stack. Findings reflect what we verified. Grades reflect the full trust pyramid: code, verification depth, and reputation. New projects cap at C until adoption is earned.

Limited view: static analysis for Python is partially covered.

How we verified

Code Verified⚡ Live Monitored: not connected

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Who stands behind it

quintonwall

Source: Glama

Is this your MCP?

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Install from

Glamanot scored here

The grade above is for the source repository. Registries can serve a different version, so we mark the ones we were not able to read.

// key findings
✅
No credential exfiltration, no sensitive file access, no obfuscation
Static analysis found nothing flowing your secrets to unexpected places.
🔐
You'll be asked for 2 credentials: OPENAI_API_KEY, MCP_TRACKING_KEY
These are read from process.env at runtime. Make sure you trust where they’ll be sent.
// environment variables
To run this server yourself, you supply these values. They go in your own MCP client configuration and stay on your machine. The secret label means the value is sensitive, not that the server mishandles it.
configVECTOR_STORE_ID
🔐 secretOPENAI_API_KEY"": "your-openai-api-key-here"
🔐 secretMCP_TRACKING_KEY
configDOCKER_CONTAINER
configDYNO
configVERCEL
configMCP_CLIENT_OVERRIDE
configMCP_ENABLE_LOCAL_LOGGING
configMCP_TELEMETRY_HASH_PROMPTS
Deployment configuration, supplied by whoever hosts the server. Users are not asked for these.
deployPORT
// quality suggestions

Tool annotations

No tools have read-only/destructive annotations

Add readOnlyHint or destructiveHint annotations to every tool so hosts can warn users before invoking.

All four hints declared on every tool

1/1 tools missing one or more hints — generate_pyairbyte_pipeline (missing: readOnlyHint, destructiveHint, idempotentHint, openWorldHint). OpenAI's directory rejects tools where any of the four hints are missing or non-boolean.

For every tool, set all four hints (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) to explicit true/false values that match the handler’s actual behaviour.

License file

No license file

Add a LICENSE file (MIT, Apache-2.0, etc.).

Tests exist

No test files found

Add tests that exercise each declared tool.

Claim the listing to review these findings one by one and send us a correction where you disagree, straight to the team. Claiming also means we tell you when the grade moves, and reach you first if we find anything urgent.

// full audit trail
The findings above are the summary. The full trail, every check we ran, each deduction, the network hosts observed and the dependency advisories, goes to verified publishers, along with an alert whenever a new one lands. Verified publishers can also review each finding and dispute it in one click. Publisher corrections have sharpened several of our checks this month, because the maintainer knows the codebase better than any scanner.
// improvement guidance — verified publishers only
We have 4 concrete improvements we can share with the publisher of this MCP. Each comes with specific guidance to raise the trust score.
// embed badge in your README
[![M8ven Score](https://m8ven.ai/badge/mcp/quintonwall/airbyte-labs-pyairbyte-mcp)](https://m8ven.ai/mcp/quintonwall/airbyte-labs-pyairbyte-mcp)
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commit: 6e6f29561f94fa6cc0d5c205193a95ae9dd66892
code hash: ccebe43b5d85341ff0dd245ab0f4de7dd4ccc3bdd517b3e852911cb454930a97
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