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notion-query-interface-proxy

A high-efficiency, read-only intermediary service layer for interacting with Notion data. It facilitates secure retrieval of Notion elements—including structured pages, raw block content, database schemas, annotations, and item metadata—by aggressively optimizing API interactions through concurrency control and call reduction techniques.

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notion-query-interface-proxy logo

Taewoong1378

MIT License

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GitHub GitHub Stars 4
NPM Weekly Downloads 0
Tools 1
Last Updated 2026-02-19

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cloudmcpreadonlynotion readonlyreadonly mcpprovides read

Optimized Notion Data Access Gateway (ReadOnly)

This specialized MCP implementation serves as a streamlined, performant server endpoint for querying Notion resources exclusively for data extraction purposes, specifically engineered to enhance the operational efficacy of Generative AI agents.

Notion ReadOnly Server MCP server

Core Design Philosophy & Performance Enhancements

This gateway prioritizes speed and robustness in data acquisition:

  • Extraction Focus: Strictly enforces read-only access patterns, safeguarding workspace integrity while exposing necessary information retrieval functions.
  • Tool Abstraction: Drastically curtails the exposed toolset from the extensive native Notion API enumeration (over 15 functions) down to 7 critical, specialized operations necessary for detailed document analysis.
  • Concurrent Fetching: Maximizes throughput by employing asynchronous processing and parallel execution when recursively fetching nested block structures, dramatically accelerating full-page synthesis.
  • Expanded Coverage: Incorporates native support for querying database blueprints, retrieving granular page attribute values, and fetching associated commentary threads.
  • AI Agent Compatibility: The deliberate reduction in available endpoints directly mitigates performance degradation observed in LLM frameworks (like Cursor or Claude) constrained by high tool inventory counts (often limited to ~40 total tools).

Toolset Consolidation

This read-only architecture presents a significantly smaller, more targeted toolkit than standard Notion integration wrappers, resulting in superior performance and better model adherence:

Standard Notion API Exposure This ReadOnly Gateway Exposure
15+ Functions 7 Essential Functions

This strategic constraint keeps the interface within performance sweet spots for model reasoning while maintaining comprehensive data access capability.

Deployment Instructions

1. Notion Integration Setup:

Navigate to https://www.notion.so/profile/integrations to establish a new internal integration or utilize an existing one.

While this server limits its capabilities to data reading, exposing workspace information to external LLM services carries inherent, albeit minor, risk. For maximum security posture, precisely configure the Integration's Capabilities under the 'Configuration' tab to grant only "Read content" permissions.

2. MCP Configuration Addition:

Integrate the following configuration stanza into your client's MCP manifest (.cursor/mcp.json or ~/Library/Application\ Support/Claude/claude_desktop_config.json):

NPM Execution Method:

{ "mcpServers": { "notionApi": { "command": "npx", "args": ["-y", "notion-readonly-mcp-server"], "env": { "OPENAPI_MCP_HEADERS": "{\"Authorization\": \"Bearer ntn_****\", \"Notion-Version\": \"2022-06-28\" }" } } } }

Docker Container Method:

{ "mcpServers": { "notionApi": { "command": "docker", "args": [ "run", "--rm", "-i", "-e", "OPENAPI_MCP_HEADERS", "taewoong1378/notion-readonly-mcp-server" ], "env": { "OPENAPI_MCP_HEADERS": "{\"Authorization\":\"Bearer ntn_****\",\"Notion-Version\":\"2022-06-28\"}" } } } }

Crucial Step: Substitute ntn_**** with your actual integration secret token obtained from the integration settings panel.

3. Workspace Resource Connection:

Verify that the target Notion pages and databases have explicitly shared access with the newly created integration via the page's three-dot menu ("Connect to integration").

Available Read-Only Functionalities

This optimized package exposes seven core, non-destructive Notion API tools:

  • API-retrieve-a-page: Fetches core page metadata.
  • API-get-block-children: Retrieves nested content structure (utilizes internal parallelism).
  • API-retrieve-a-block: Fetches details for an isolated content block.
  • API-retrieve-a-database: Retrieves the schema and metadata of a database entity.
  • API-retrieve-a-comment: Fetches commentary threads attached to a page or block.
  • API-retrieve-a-page-property: Extracts the specific value of a designated page attribute.
  • API-get-one-pager: NEW FEATURE! Recursively aggregates an entire page hierarchy (blocks, embedded databases, comments) into a single, comprehensive payload.

By narrowing the interface to these 7 functions (down from the standard 15+), we achieve:

  1. Superior execution speed within constrained AI environments.
  2. Reduced ambiguity and decision latency for the reasoning engine.
  3. Faster overall API interaction cycles.
  4. A significantly reduced security exposure profile.

The 'One Pager' Comprehensive Retrieval Tool

The introduction of API-get-one-pager revolutionizes content ingestion by consolidating multi-step page traversal into one atomic action:

  • Full Structural Traversal: Automatically navigates and collects the complete hierarchy of nested blocks.
  • Maximum Concurrency: Leverages parallel requests to pull sibling blocks and deeply nested elements simultaneously.
  • Adaptive Caching: Implements internal data storage to suppress redundant lookups within a single request context.
  • Holistic Data Capture: Guarantees inclusion of pages, blocks, database contents, comments, and property details.
  • Depth Control: Allows users to define a maxDepth parameter to balance data richness against retrieval time.

'One Pager' Invocation Schema

{ "page_id": "YOUR_PAGE_ID", "maxDepth": 5,
"includeDatabases": true,
"includeComments": true,
"includeProperties": true
}

This tool is invaluable for AI assistants requiring immediate, deep context on a Notion document without incurring the overhead of dozens of sequential API probes.

Concurrency Management

The server employs sophisticated concurrent request handling:

  • Bulk requests are scheduled for parallel execution.
  • Automatic management of block children pagination.
  • Results aggregation occurs efficiently prior to serialization.
  • Internal operational logging provides process transparency without polluting the final data response structure.

Usage Illustration

  1. Page Content Extraction:

Retrieve the entirety of content associated with identifier 1a6b35e6e67f802fa7e1d27686f017f2

The system uses parallel fetching to resolve all constituent blocks quickly.

  1. Database Schema Inquiry:

Outline the structure of database 8a6b35e6e67f802fa7e1d27686f017f2

Development & Maintenance

Build Commands:

pnpm build

Execution:

pnpm dev

Licensing

MIT License applies.

AI Reasoning Performance Gain

AI reasoning systems face inherent limitations regarding tool inventory size:

  • Models often struggle to utilize or even reference tools beyond a threshold (typically 40 total functions).
  • Over-tooling introduces noise, increasing response latency and decision-making errors.

This intentionally restricted, read-only gateway directly addresses these architectural constraints by retaining functional necessity while simplifying the interface, leading to:

  • More predictable and faster execution times from AI clients.
  • Higher reliability when synthesizing answers based on Notion data.
  • Optimized performance derived from a focused, lean API surface.

WIKIPEDIA: Cloud computing is "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on-demand," according to ISO. It is commonly referred to as "the cloud".

== Characteristics == In 2011, the National Institute of Standards and Technology (NIST) identified five "essential characteristics" for cloud systems. Below are the exact definitions according to NIST:

On-demand self-service: "A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider." Broad network access: "Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations)." Resource pooling: " The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand." Rapid elasticity: "Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time." Measured service: "Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. By 2023, the International Organization for Standardization (ISO) had expanded and refined the list.

== History ==

The history of cloud computing extends to the 1960s, with the initial concepts of time-sharing becoming popularized via remote job entry (RJE). The "data center" model, where users submitted jobs to operators to run on mainframes, was predominantly used during this era. This was a time of exploration and experimentation with ways to make large-scale computing power available to more users through time-sharing, optimizing the infrastructure, platform, and applications, and increasing efficiency for end users. The "cloud" metaphor for virtualized services dates to 1994, when it was used by General Magic for the universe of "places" that mobile agents in the Telescript environment could "go". The metaphor is credited to David Hoffman, a General Magic communications specialist, based on its long-standing use in networking and telecom. The expression cloud computing became more widely known in 1996 when Compaq Computer Corporation drew up a business plan for future computing and the Internet. The company's ambition was to superch

See Also

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