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AI OS MCP Server

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AI OS MCP Server

Exposes Felix's AI OS (synced in Notion) as MCP tools, so any MCP-compatible AI client can query it directly instead of scraping a public link.

Honest limitation: this was written without the ability to actually npm install or compile-test it (no network access in the environment it was built in). The code follows the current MCP TypeScript SDK and Notion client API shapes correctly as far as known, but run the build step yourself and check for errors before trusting it — don't assume it's flawless just because it was written carefully.

Tools It Exposes

  • search_vault(query) — keyword search across the whole AI OS

  • get_page(page_name) — full content of one named page

  • list_projects() — the 10_Projects index page's current contents

  • get_project_status(project_name) — one project's current status specifically

Related MCP server: nooon

Setup

1. Create a Notion integration

Go to notion.so/my-integrations → New integration → name it (e.g. "AI OS MCP") → copy the "Internal Integration Secret" (starts with secret_ or ntn_).

2. Share the AI OS with it

Open the AI OS root page in Notion → ... menu → Connections → add the integration you just created. This is separate from "Share to web" — it's what actually lets the integration's API calls see the pages.

3. Set the API key

cp .env.example .env
# paste your key into .env

4. Build and run with Docker

docker compose build
docker compose run --rm ai-os-mcp

This runs the server over stdio — it's meant to be launched by an MCP client (Claude Desktop, etc.), not left running standalone in a terminal long-term.

5. Point an MCP client at it

For Claude Desktop, add to its MCP config (claude_desktop_config.json):

{
  "mcpServers": {
    "ai-os": {
      "command": "docker",
      "args": ["compose", "-f", "/full/path/to/ai-os-mcp/docker-compose.yml", "run", "--rm", "ai-os-mcp"]
    }
  }
}

Things Worth Checking Before Trusting It

  • Does npm install succeed cleanly, or are there version mismatches with the SDK?

  • Does npm run build compile without errors?

  • Does search_vault actually return results — this depends on the integration having been shared with the right pages (step 2).

  • Table content in get_page may render roughly — Notion's block API is nested and this pulls table rows one level deep, not deeply verified.

What This Doesn't Do

No writing/editing — read-only by design, so it can't accidentally modify the vault. No automation trigger — still has to be invoked by an MCP client on request, consistent with the AI OS's "manual, chat-triggered" execution decision.

Available Tools

4 tools
get_pageA

Fetch the full content of a specific named page from the AI OS, e.g. 'GetClean', 'Knowledge Core', 'Fulfillment Workflow'.

ParametersJSON Schema
NameRequiredDescriptionDefault
page_nameYesThe exact or partial page title

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations to rely on, the description states that the tool fetches full content, which conveys read-only intent. However, it does not explain partial-match behavior, ambiguity handling, or what happens when no content exists.

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?

Single sentence, front-loaded with the action and resource, and the examples earn their place by clarifying what counts as a page. No redundant content.

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 one-parameter fetch tool, the description is largely sufficient: it names the resource, the action, and the sort of value pages can have. Lacking an output schema or annotations, a brief note on return format would make it fully complete, but the constraint set is minimal.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already defines page_name as an exact or partial page title. The description adds concrete examples but no additional semantic meaning beyond the schema.

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?

Description states a specific verb/resource ('Fetch the full content... page') and distinguishes the target (pages) from sibling tools (vault, projects, project status). Examples clarify the intended use case.

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

Usage Guidelines3/5

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

Usage is implied by 'specific named page' and examples, but no explicit guidance is given about when to prefer this over search_vault or get_project_status. The distinction is inferable but not stated.

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

get_project_statusA

Fetch the current status of one specific project by name, e.g. 'QuickTurnaroundGigs' or 'GetClean'.

ParametersJSON Schema
NameRequiredDescriptionDefault
project_nameYesThe project's name

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the burden, and it adequately conveys that this is a read-only fetch, not a mutation. However, it does not disclose behavior for unknown project names, exact matching, or the shape of the returned status value.

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?

One concise sentence packs in the verb, resource, scope, and examples without any waste. Every part of the sentence adds meaning.

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 simple one-parameter read-only tool, this description is largely sufficient. The main gap is that no output schema is present and the description does not describe what the returned status payload looks like, but the tool name and verb make the expected outcome reasonably clear.

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 schema already provides 100% description coverage for project_name. The description adds extra semantics by emphasizing that the parameter is the project name itself and giving realistic examples, which helps an agent format the value correctly.

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 uses a specific verb ('Fetch'), names the exact resource ('current status of one specific project by name'), and gives concrete examples ('QuickTurnaroundGigs', 'GetClean'). This clearly differentiates it from siblings like list_projects, which would list multiple projects.

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 clearly implies when to use it: when you need the status for a single known project. It does not explicitly name alternatives or say when not to use it, but the scope is clear enough for an agent to select it over a list/search tool.

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

list_projectsA

List all active projects in the AI OS with a one-line status each, by reading the '10_Projects' index page.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it does well by revealing it is a read of the '10_Projects' index page, that only active projects are included, and that each project comes with one status line. This gives the agent a meaningful behavioral model without misleading details.

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 a single tight sentence that front-loads the action and output. It adds only high-signal details: scope ('active'), output shape ('one-line status each'), and data source ('10_Projects' index). No filler.

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 zero-parameter, no-output-schema tool, the description is quite complete: it specifies what is returned, which records are included, and where the data comes from. It could more explicitly label the operation as read-only, but 'by reading the index page' carries most of that weight.

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 no parameters, and the input schema confirms this. The description appropriately implies that no filtering or arguments are needed because it lists 'all' active projects, which is consistent with the empty schema.

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 states a concrete verb ('List'), a clear resource ('all active projects in the AI OS'), and a distinctive output ('one-line status each'). It also names the mechanism ('reading the 10_Projects index page'), which separates it from sibling tools like get_project_status and search_vault.

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

Usage Guidelines3/5

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

The description makes the core use case clear: use this tool when you need an inventory of all active projects. However, it does not explicitly mention when to avoid it or when to prefer a sibling like get_project_status or get_page, so the guidance is implied rather than stated.

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

search_vaultA

Search Felix's AI OS (synced in Notion) for pages matching a keyword or topic. Returns page titles and URLs, not full content — use get_page for that.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch term, e.g. a project name or topic

TDQS

A4.3/5.0
Behavior4/5

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

There are no annotations, so the description must carry behavior. It does so by saying the tool searches a synced Notion source and returns only titles and URLs, not full content. This is meaningful behavioral transparency, but it leaves out details like rate limits, staleness, and search coverage, so it isn't quite 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no dead weight. The first states the action and resource; the second states return limits and routes to get_page. Information density is high and the details are in order of importance.

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

Completeness5/5

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

A single-parameter search tool with no output schema; the description sufficiently covers what to pass, what to expect back, and the upstream alternative for full content. There's nothing critical missing for an agent to invoke it correctly.

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

Parameters3/5

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

Schema description coverage is 100% and the schema already explains query as 'Search term, e.g. a project name or topic.' The description's phrase 'keyword or topic' restates the schema rather than adding new semantics, so the baseline of 3 is appropriate.

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 starts with a clear verb and resource — search Felix's AI OS for pages matching a keyword or topic. It explicitly states what is returned (titles and URLs) and what it is not (full content), distinguishing it from get_page.

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?

It gives clear context for when to search — when you need titles/URLs of pages matching a keyword — and explicitly points to get_page for full content. However, it doesn't mention when list_projects or get_project_status might be preferable, so exclusions are not exhaustive.

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.

  1. 4 tool updatesv1.0.0
    • First observedget_page
    • First observedget_project_status
    • First observedlist_projects
    • First observedsearch_vault
Deploy Server
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TDQS

A4.1/5.0

Scored across 4 tools

Disambiguation4/5

search_vault and get_page are clearly separated by search-results vs full-content, and list_projects vs get_project_status are also distinct in scope. The only minor ambiguity is that get_page could also be used to fetch project content that contains status, making the boundary slightly less obvious.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: search_vault, get_page, list_projects, get_project_status. Verbs are lowercase and the naming style is uniform.

Tool Count5/5

With only 4 tools, the server is compact and well-scoped for searching and viewing pages and project statuses. Each tool serves a distinct retrieval purpose and none feels like filler.

Completeness4/5

The read/search surface is well covered: search, get page, list projects, and get specific status all work together without dead ends. The main gap is the lack of write or update operations, so if modifying pages or project statuses is expected, the toolset would be incomplete.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Related MCP Connectors

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