Apple Photos MCP
This server gives an AI agent real, local access to your Apple Photos library to search, inspect, organize, and export your photos without a backend.
Search photos by visual description, text/OCR, place, person, date, album, kind, favorites, and screenshot status.
Look at photos by rendering previews as images so the agent can visually confirm results.
Get photo info including metadata, ML labels, place, albums, faces, and recognized text.
View library stats such as totals, albums, named people, and iCloud-only items.
List visual vocabulary to discover Apple's recognized search terms.
Export full-quality originals to a local folder.
Organize the library: favorite/unfavorite, add to albums, add keywords, set titles/descriptions, and archive photos (archive requires confirmation).
Diagnose setup with
doctorto check permissions, library access, and index status.
Provides tools for searching, viewing, organizing, and exporting photos from the user's Apple Photos library, leveraging Apple's on-device machine-learning index of labels, text, places, people, and more.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Apple Photos MCPFind the receipt from that restaurant in Cambodia"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Apple Photos MCP + CLI
Apple Photos MCP server and CLI for Claude Code and AI agents. 13 tools to search, look at, organise and export your own library, entirely on your Mac.
Give any AI agent real access to your own Apple Photos library, so it can find, see, organise and export your photos instead of guessing.
Built and maintained by Navid Moazzez.
Everything runs on your Mac. There is no backend.
Two ways to use it
Command line
apple-photos-cli in your terminal, for scripting, cron, pipes, or a quick
question without opening anything:
apple-photos-cli # every command, one line each
apple-photos-cli library-stats --json # real totals, one call
apple-photos-cli search-photos "sunset" --limit 5 --screenshots exclude
apple-photos-cli photo-info --refs IMG_2073.MOV
apple-photos-cli export-originals --refs <uuid> --directory ./out
apple-photos-cli <command> --help # what any command takes--json gives JSON, --compact puts it on one line, --select total,videos
keeps only the fields you name, and errors are JSON on stderr whichever you
pick. --confirm is the shell spelling of the confirmation archiving requires.
MCP server, for AI agents
apple-photos-mcp is what Claude Code, Claude Desktop, Cursor and the rest
launch. You never run it by hand:
claude mcp add apple-photos -- npx -y @thenavidm/apple-photos-mcp-cli@latestThen just ask: "which receipts do I have from Vietnam?"
Which one
Where you are | What you can reach |
An agent that can run shell commands, like Claude Code or Cursor | Both. The CLI is the cheaper one: it costs nothing until you type it |
A terminal, a script, cron or CI | The CLI only. There is no MCP client in a shell |
They are the same program reading the same tool definitions, so anything one can do, the other can. A test asserts they cannot drift.
Related MCP server: iOS MCP Server
Features
Every tool is both a command and an MCP tool, with the same name. The command is the tool name with dashes.
Capability | CLI command | MCP tool |
Search the whole library |
|
|
Actually look at photos |
|
|
Full metadata, camera and lens |
|
|
Real totals in one call |
|
|
What Apple can search for |
|
|
Export originals to disk |
|
|
Favourite or unfavourite |
|
|
Title and description |
|
|
Keywords and albums |
|
|
Move into an archive album |
|
|
Check the setup |
|
|
Find the right command |
| not a tool |
All 13 with their arguments are in section 6.
Output and exit codes
Results on stdout, errors on stderr as JSON, so one parse handles both.
Flag | Result |
none | pretty JSON |
| JSON, always |
| the same JSON on one line |
| keep only these fields |
Code | Means |
| it worked |
| it failed: the engine refused, a guard blocked it, nothing matched |
| it was typed wrong: a missing flag, a bad value, an unknown option |
Which surface, and what each costs
An MCP server is charged on every turn; a CLI costs nothing until it is called.
The tools/list payload for these 13 tools is about 2,800 tokens, plus the
server instructions, on every turn of every conversation whether you use it or
not. The CLI is free until you run something, which is why both exist.
Contents
Section | ||
Every tool, both surfaces | ||
What scripts branch on | ||
1 | Real prompts, not features | |
2 | One command, no credential | |
3 | One permission, once | |
4 | Every client, copy and paste | |
5 |
| |
6 | All 13 | |
7 | What asks, what does not | |
8 | The things that surprise people | |
9 | What is stored, and where | |
10 | Every setting | |
11 | When something breaks | |
12 | Start here if you are new |
1. What you can ask it 💬
Find my photo of that whiteboard from the Bogota trip.
Which receipts do I have from Vietnam?
Show me videos from Dubai in 2024.
Do I have a picture of my passport?
How many photos do I actually have, and how many are just screenshots?
Pull up the sunset shots from Miami Beach.
Add these five to an album called Best of Bali.
Export the original of that one to my Desktop.
Which places show up most in my library?
The first one is the point. Your library already knows what is in every photo, because Apple ran machine learning across all of it on your device and wrote the results into the library. Photos gives you a search box for that. This gives an agent the entire index, so it can filter, cross-reference and then actually look at the results before answering.
2. Quick install ⚡
macOS, and Node 20 or newer.
npx -y @thenavidm/apple-photos-mcp-cli@latest --versionThat is the whole install. npx fetches it on demand, so there is nothing to
update later.
The engine underneath is Python, because osxphotos and photoscript are the
only libraries that can read a Photos library and both are Python-only. You do
not install them: uv fetches them on first run and
caches them. If you do not have uv:
curl -LsSf https://astral.sh/uv/install.sh | shNo account, no API key, no credential. macOS will ask for permission the first time something reads the library.
3. Setup 🔑
There is no login. The only setup is one macOS permission, because your Photos library is protected and an app has to be allowed to read it.
Full Disk Access, once:
Open System Settings.
Go to Privacy & Security, then Full Disk Access.
Click + and add the app that will run the server:
Claude Desktop, if you use Claude Desktop.
Terminal or iTerm, if you use Claude Code.
Cursor, VS Code or Windsurf, if you use one of those.
Make sure its toggle is on.
Quit that app completely and reopen it. Cmd+Q, not just closing the window. The permission is only picked up on a fresh launch.
That is it. Skipping step 5 is the single most common reason this looks broken.
A second permission appears later, only if you organize. The first time a tool changes something, macOS asks whether the app may control Photos. Click OK. If you miss it, it is under Privacy & Security, then Automation.
Have an agent do it
The agent cannot grant the permission for you, because macOS deliberately requires a human. It can do everything either side of it.
Paste this into Claude Code, Cursor, or any agent with terminal access:
Set up apple-photos-mcp for me.
1. Install it and run `doctor`, then tell me exactly what failed.
2. Walk me through granting Full Disk Access one step at a time,
naming the app I need to add. Stop and wait for me to confirm,
and remind me to fully quit the app with Cmd+Q afterwards.
3. Run `doctor` again and show me how many photos it can see.
4. Then add it to my MCP client config.4. Connect your client 🔌
Claude Code
claude mcp add apple-photos -s user \
-- npx -y @thenavidm/apple-photos-mcp-cli@latest-s user makes it available in every project rather than just the current one.
Check it registered:
claude mcp listClaude Desktop
Open Settings, then Developer, then Edit Config. That opens the file in your editor.
Platform | Path |
macOS |
|
open -e ~/Library/Application\ Support/Claude/claude_desktop_config.jsonApple Photos only exists on macOS, so there is no Windows or Linux path here.
If the file is empty or new, paste all of this:
{
"mcpServers": {
"apple-photos": {
"command": "npx",
"args": ["-y", "@thenavidm/apple-photos-mcp-cli@latest"]
}
}
}If it already has other servers, add only the "apple-photos" block inside the existing "mcpServers" object, and put a comma after the previous server's closing brace. One misplaced comma invalidates the file, and then every server disappears, not just this one.
Claude Desktop does not inherit your shell PATH. Ifnpx is not found, run which npx in a terminal and use that absolute path as "command".
Quit Claude Desktop completely with Cmd+Q and reopen it.
Logs, when you need them:
tail -f ~/Library/Logs/Claude/mcp-server-apple-photos.logThere is also a one-click .mcpb bundle on the releases page, installed through Settings, then Extensions, then Install Extension. It carries the server and the Python engine; uv still fetches the engine's dependencies on first run, because they build pyobjc and only work on macOS.
Cursor
Cursor Settings, then MCP, then Add new global MCP server. That opens ~/.cursor/mcp.json. Same JSON shape as Claude Desktop, same mcpServers key. Save, then click reload next to the server.
For one project instead of globally, use .cursor/mcp.json in that project.
Windsurf
Windsurf Settings, then Cascade, then Model Context Protocol (MCP), then Add Server. That opens ~/.codeium/windsurf/mcp_config.json. Same shape, key mcpServers. Save, then Refresh.
VS Code
.vscode/mcp.json. The key is servers, not mcpServers, and each entry takes a type:
{
"servers": {
"apple-photos": {
"type": "stdio",
"command": "npx",
"args": ["-y", "@thenavidm/apple-photos-mcp-cli@latest"]
}
}
}Reload the window: Cmd+Shift+P, then Developer: Reload Window.
Codex CLI
~/.codex/config.toml:
[mcp_servers.apple-photos]
command = "npx"
args = ["-y", "@thenavidm/apple-photos-mcp-cli@latest"]Gemini CLI
~/.gemini/settings.json, key mcpServers, same shape as Claude Desktop.
Everything else
Any stdio MCP client takes the same two things: the command npx, and the args above.
claude.ai on the web, with a relay
claude.ai runs connectors from Anthropic's cloud, so it cannot start a process on your Mac. That does not make it impossible, it makes it a two-part job.
This package ships stdio only. To reach it from a browser you put a small remote MCP server in front of it, and have a local agent on the Mac poll that server for queued work and post the results back. The cloud half is reachable from claude.ai, the Mac half holds the library, and no port on your machine is ever exposed to the internet.
That relay is not included here. It is a separate deployment with its own hosting and auth, so it is out of scope for a package you install with one command. Everything needed on the Mac side is already in this server.
5. Check it worked 🩺
npx -y @thenavidm/apple-photos-mcp-cli@latest doctorOr just ask your agent: "run doctor on apple photos".
Healthy output includes a line like:
{
"check": "index",
"ok": true,
"detail": "37129 assets indexed (35983 with ML labels, 10807 with readable text)"
}The two things that actually fail:
It says | Do this |
| Section 3, and remember Cmd+Q |
| Open Photos once, or set |
6. Tools 🛠️
Finding and seeing
Tool | What it does |
| Search your entire library by look, text, place, person, date |
| Render photos so the agent can actually see them |
| Everything known about specific items, including text read inside them |
| The visual words this library knows |
The library itself
Tool | What it does |
| Totals, albums, named people, how much is iCloud only |
| Diagnose setup |
Organizing
Tool | What it does |
| Heart items, or remove the heart |
| Add to an album, creating it if needed |
| Add keywords, keeping existing ones |
| Set one item's title |
| Set one item's caption |
| Move to an archive album. Needs |
Getting files out
Tool | What it does |
| Full quality originals to a folder on your Mac |
7. Working safely 🛡️
Organizing works out of the box, because that is the point of the tool. Three things guard it.
Nothing can delete a photo. macOS does not expose scripted deletion to any application, and this server does not try to route around that. archive_photos moves items into an album so they leave your main view, and you empty that album yourself.
archive_photos is the only tool that asks. It needs confirm: true. Everything else it can do is one click to undo in Photos, and requiring confirmation on all of them would just teach a model to pass confirm reflexively, including on the one that matters.
APPLE_PHOTOS_READ_ONLY=1 removes every write tool. They vanish from the tool list rather than erroring when called, because a model cannot call a tool it cannot see. This is the right setting for an agent working unattended.
APPLE_PHOTOS_AUDIT_LOG=~/.apple-photos-mcp/writes.jsonl records one JSON line per attempted write, allowed and blocked alike.
Every tool carries MCP annotations, so a client can decide what to auto-approve: reads are marked read-only, organizing is marked non-destructive, and archive_photos is marked destructive. Nothing is marked open-world, because nothing here leaves your machine.
On prompt injection: text read out of your own photos, mostly OCR, is content you photographed rather than content a stranger sent you, so the exposure is far smaller than a server that reads a public feed. It is not zero. A screenshot of a web page can contain instructions. READ_ONLY=1 is the real defense for unattended work.
8. What Apple Photos actually does 📸
The part that makes this worth more than the Photos search box.
Apple already indexed everything, on your device. Every photo carries scene labels from a classifier with a closed vocabulary of roughly 1,500 words, the text its OCR read inside the image, a guess at the activity, the venue type, and a reverse geocoded place. None of that needs Photos.app running to read.
Your typed metadata is almost certainly empty. In the 37,129 item library this was built against, 3 items had a title and 3 had keywords. So searching for what you call a photo will fail. Search for what the photo looks like.
The vocabulary is closed, and that is a real limit. Apple knows "Sunset" but not "golden hour". Ask for a word it has never heard of and the response says so in unmatched_terms and suggests words that do exist, rather than quietly returning something that looks like a match. list_vocabulary shows every term it knows.
Text found inside an image is weak evidence. A screenshot full of words will match almost any query if you let it, and about one in five items in a typical library is a screenshot. Matches resting only on OCR are scored down, and screenshots compete at a discount unless you actually asked for a screenshot or a document.
Most of your photos are not on your Mac. iCloud keeps the originals in the cloud and leaves thumbnails behind. In the test library, 36,996 of 37,129 assets had no local original. Previews still work, because they read Apple's cached thumbnails. Exporting an original downloads it first, which is why exporting is slow and looking is not.
Faces only work if you named them. Photos recognizes faces on its own, but they are anonymous until you attach a name in the People album. Searching for a person only finds people you have actually named.
9. Your data 💾
Nothing leaves your Mac. There is no backend, no account, and no telemetry.
What | Where | Contents |
Search index |
| Compressed filenames, dates, labels, places and OCR text. No image data. |
Previews |
| Downscaled JPEGs. Safe to delete any time. |
Audit log | Only if you set | One line per attempted write. |
Exports |
| Only what you explicitly export. |
Delete ~/.apple-photos-mcp/ to clear all of it.
Previews are passed to whichever model you are talking to, exactly like attaching a photo to a chat yourself. That is the purpose of look_at_photos, and it is the only path by which image data leaves your machine.
10. Configuration ⚙️
Every setting is an environment variable, set in your client's env block.
Variable | Default | What it does |
| off | Removes every write tool from the tool list |
| off | Path for one JSON line per attempted write |
| last opened | Path to a specific |
|
| Default export folder |
|
| Where previews are cached |
|
| Longest edge of a preview |
|
| Most items rendered per |
|
| Most items a single write may touch |
|
| Album |
11. Troubleshooting 🔧
Run doctor before guessing. It names which of these it is.
Symptom | Cause | Fix |
Permission denied reading the library | No Full Disk Access | Section 3. Quit the app with Cmd+Q, not just the window. |
Server does not appear in the client | Bad JSON, usually a comma | Paste the config into a JSON validator. One bad comma hides every server. |
| The app cannot see your shell PATH | Use the absolute path from |
Write tools are missing |
| Unset it and restart the client |
Reads work, writes fail | Photos automation not approved | Approve the popup, or Privacy & Security, then Automation |
First search takes half a minute | Building the index | One time, about 25 seconds for 37,000 items. Cached afterwards. |
A photo has no preview | iCloud only, with no cached thumbnail | Open it once in Photos, or export it |
Search finds nothing for an obvious word | Apple has no such label | Check |
12. FAQ ❓
A standard way to give an AI assistant real access to a tool, so it can act rather than guess. You install it once, your assistant gains a set of tools, and it works the same in Claude, Cursor, Codex and anything else that speaks MCP.
The photo app built into every Mac, iPhone and iPad. It stores your photos and videos and, on the device itself, runs machine learning over them to work out what is in each one. That analysis is what this server searches.
You need to run one install command in Terminal and grant one macOS permission. After that you talk to it in plain English. Section 3 has an agent-guided path that walks you through the permission step by step.
No, and nobody. The library is read on your Mac and there is no backend, no account and no telemetry.
The one exception worth knowing: when you ask your assistant to look at a photo, that preview is sent to whichever model you are already chatting with, exactly as if you had attached it yourself. That is what makes "find my photo of X" work.
Photos gives you one search box. This gives an agent the entire index at once, so it can combine things the app cannot: a scene label, a place, a date range and a person in a single query, then look at the results and tell you which one you meant.
It can also act on what it finds. "Find every receipt from Vietnam last year and put them in an album" is one sentence here and a long afternoon in the app.
It cannot delete them at all. macOS does not allow any app to delete photos by script, so there is no delete tool and no way to add one.
The closest thing is archive_photos, which moves items into an album called "Archived by Claude" so they leave your main view. It refuses to run without confirm: true, and you empty that album yourself in Photos. Everything else it can change, favorites, albums, keywords, titles, is one click to undo.
Yes. Set APPLE_PHOTOS_READ_ONLY=1 in your client config and every write tool disappears from the tool list. This is the right setting if an agent is running unattended.
No. It is MIT licensed and it talks to nothing but your own Mac, so there is no API bill and no subscription. Your assistant's own usage is whatever you already pay for it.
It works with any client that runs a local MCP server: Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, Codex CLI, Gemini CLI.
For claude.ai in a browser you need one extra piece. Those connectors run in Anthropic's cloud and cannot start anything on your Mac, so it takes a small remote server that queues the work plus a local agent that carries it out and posts results back. It works, it is just a separate deployment rather than something this package installs for you.
Yes for searching and looking. Apple keeps thumbnails on the Mac even when the full size original is in the cloud, and previews read those, so they are fast.
Exporting a full quality original downloads it first, which is much slower. In the library this was built against, 36,996 of 37,129 assets were iCloud only and every preview still rendered.
The first search builds an index, which takes about 25 seconds for a 37,000 item library. After that it is served from a small cache and searches take about a second. Reading does not involve Photos.app at all, so nothing has to be open.
Delete the server from your client's config, or run claude mcp remove apple-photos in Claude Code. Then delete ~/.apple-photos-mcp/ to remove the index and cached previews. Nothing is left behind, and nothing in your Photos library is changed by removing it.
Questions
Run into a problem or have a question? Open an issue and I will help.
About the author
Navid Moazzez is a leading AI business strategist, and the host of the AI Creator Summit, watched by 100,000+ creators. He helps creators and founders master AI and build their own AI Operating System (AI OS) to automate their business and life. This Apple Photos MCP server is one piece of that system.
Links
Personal website: navid.me
Link in bio: navid.bio
Navid Media: navid.media
YouTube: @thenavidm and @thenavidai
X: @thenavidm
Instagram: @thenavidm
LinkedIn: thenavidm
If this is useful, star the repo and come say hi on X.
Dependencies
Package | License | Why |
MIT | The MCP protocol implementation | |
MIT | Reads the Photos library database and Apple's on-device ML metadata | |
MIT | Drives Photos.app for writes |
License
MIT. Free to use, modify, and share.
Not affiliated with, endorsed by, or connected to Apple Inc. Apple, macOS, Photos and iCloud are trademarks of Apple Inc.
© 2026 NM Media. Made with ❤️ by Navid Moazzez.
Available Tools
13 toolsadd_keywordsAIdempotent
Add keywords to photos, keeping the ones already there.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | ||
| keywords | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare non-read-only, non-destructive, and idempotent behavior. The description adds the key behavioral detail that existing keywords are preserved, which is essential for distinguishing append semantics. No contradiction exists.
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?
A single, efficient sentence that conveys the core action and behavior without waste. It is front-loaded with the verb and resource.
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?
The tool is simple and annotations cover safety, but the description omits any mention of return values or usage context. The presence of an output schema reduces the need to explain returns, yet parameter ambiguity remains a gap.
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 description coverage is 0%, and the description provides no additional meaning for 'refs' or 'keywords'. While the parameter names are somewhat self-explanatory, the description does not compensate for the missing schema descriptions.
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 states a specific verb ('Add'), a resource ('keywords to photos'), and a crucial scope qualifier ('keeping the ones already there'). This clearly distinguishes it from sibling tools like set_photo_title or add_to_album.
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?
No guidance is provided on when to use this tool versus alternatives. The description does not mention context, exclusions, or contrast with other add/set operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
add_to_albumBIdempotent
Add photos to an album, creating it if needed.
Args: album: Album name. refs: Photo uuids or exact filenames.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | ||
| album | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include idempotentHint: true and destructiveHint: false, but the description adds the behavior of creating the album if it does not exist, which is useful. Beyond that, no behavioral traits like ownership or permissions are disclosed, but given the annotations, the description contributes some context.
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 very concise and front-loaded with the main purpose, followed by parameter explanations. The 'Args' section is clear and easy to scan, though it's slightly generated but lacks additional context that might be missing but not overly lengthy.
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's simplicity (2 parameters, idempotent and not destructive) an output schema exists, the description is adequate. However, it does not mention any side effects like album creation might fail if album name is invalid, nor how the output schema or return value is used. Still, it's acceptable for a basic operation.
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?
With schema description coverage at 0%, the description's brief 'album: Album name' and 'refs: Photo uuids or exact filenames' adds meaning. However, it is minimal and doesn't detail expected format for album names or nuances like whether refs must be list of exact matches. It partially compensates for the schema gap.
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 verb ('Add photos') and resource ('to an album'), and mentions creating the album if needed. It distinguishes the tool's purpose from siblings like search_photos or photo_info, though it doesn't explicitly contrast with them.
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 no guidance on when to use this tool versus alternatives, such as add_keywords or set_photo_title. It lacks context about typical workflows or conditions under which adding to an album is appropriate versus not.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
archive_photosADestructive
The closest thing to deleting that macOS allows.
Moves photos into an archive album so they leave the main library view. No app, including this one, can permanently delete photos by script, so the user empties that album by hand.
Args: refs: Photo uuids or exact filenames. confirm: The user reads this as deleting their photos. Set true only when they have actually asked for these specific items to go.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | ||
| confirm | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description richly discloses behavioral traits: it is not permanent deletion, it moves items to archive, and the user's perception matters ('the user reads this as deleting their photos'). It also explains the platform limitation (macOS) and that the user must manually empty the album. This goes far beyond the annotations (destructiveHint: true) by clarifying the exact scope of destructiveness and the irreversible-like nature.
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 well-structured and front-loaded with the key warning ('closest thing to deleting'), followed by how it works, then parameter explanations. Every sentence earns its place. Slight verbosity in the line breaks but overall compact. Minor deduction for the blank lines and the informal 'the user reads this as...' phrasing, but it is still tight.
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 destructive tool with two parameters and an output schema present, the description covers the critical context: what it does, how it differs from deletion, when to set confirm, and the operational consequence (user manually empties). The output schema handles return values. This is complete for an agent to decide when and how to invoke it.
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 schema covers 0% of parameter semantics, so the description must compensate. The description explains both parameters: 'refs' as photo UUIDs or exact filenames, and 'confirm' with a clear usage rule. This adds meaning beyond the bare schema types. It doesn't specify exact file name formats or UUID format, but enough for an agent to use it correctly.
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 moves photos into an archive album, removing them from the main library view, and explicitly frames it as 'the closest thing to deleting that macOS allows'. This is a specific verb+resource with a clear effect, and it distinguishes itself from direct deletion and implies why it exists (macOS restriction).
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 usage guidance: it states that no app can permanently delete photos, so the user must empty the archive album by hand. It also provides explicit instructions for the 'confirm' parameter: set true only when the user has actually asked for these specific items to go. This tells the agent both when and when not to use the tool, and distinguishes it from other photo tools by its destructive/archive nature.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
doctorARead-onlyIdempotent
Diagnose setup: macOS, Full Disk Access, the library, the index, and whether writes are enabled. Run this first when anything misbehaves.
| 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?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering the safety profile. The description adds valuable context by listing the specific aspects being diagnosed, which goes beyond the annotations and clarifies the tool's scope.
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 sentences with no filler. The first sentence lists the diagnostic targets, the second gives a direct usage directive. Every word earns its place, and the most important action ('Diagnose') 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?
For a zero-parameter diagnostic tool with rich annotations and an output schema, the description fully covers purpose, scope, and usage timing. Nothing essential is missing for an agent to select and invoke this tool correctly.
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?
This tool has zero parameters, so the baseline is 4. The description correctly avoids inventing parameter details and instead focuses on what the tool checks, which is sufficient for a no-argument diagnostic.
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?
Description uses a specific verb ('Diagnose') and names the exact resources it checks: macOS, Full Disk Access, library, index, and write status. This clearly differentiates it from sibling tools like search_photos or library_stats, which focus on other operations.
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?
Explicitly states when to use it: 'Run this first when anything misbehaves.' This gives clear timing guidance. It lacks explicit when-not-to-use or alternative tool mentions, but the context is still strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
export_originalsAIdempotent
Export full-quality originals to a folder on this Mac.
Slow for iCloud-only assets: each one is downloaded first. Nothing is uploaded anywhere.
Args: refs: Photo uuids or exact filenames. directory: Absolute destination folder. Defaults to ~/Downloads/Photos Exports.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | ||
| directory | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by disclosing performance behavior ('Slow for iCloud-only assets: each one is downloaded first') and privacy behavior ('Nothing is uploaded anywhere'). It also implicitly conveys that this is a non-destructive copy operation, consistent with destructiveHint=false. It does not contradict annotations and adds useful context about side effects.
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 compact and front-loaded: the first sentence states the core purpose. The subsequent sentence adds a crucial performance caveat in plain language. The Args section is short, scannable, and uses line breaks for each parameter. Every sentence earns its place without fluff.
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?
The description covers the essential calling details (refs, destination, iCloud caveat, setup) and is backed by annotations for safety and idempotency. It omits minor operational specifics such as whether the directory is created automatically or how duplicate filenames are handled, but these are not critical for basic invocation. The presence of an output schema also lessens the need to describe return values.
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 description coverage is 0%, but the tool description includes a dedicated Args section that explains both parameters in non-schema terms: refs accepts 'Photo uuids or exact filenames', directory is described as 'Absolute destination folder' with an explicit default. This fully compensates for the missing schema descriptions and gives the agent actionable format guidance beyond the raw property types.
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 states a specific action ('Export'), resource ('full-quality originals'), and destination ('folder on this Mac'). This is unambiguous and distinguishes it from sibling tools that search, view, archive, or modify photos. No other sibling performs local copying of originals, so the purpose is uniquely and clearly identified.
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 clearly implies the tool is used whenever full-quality photos need to be downloaded as files to this Mac, and it adds useful context about slowness for iCloud-only assets. However, it never explicitly states when to use it over alternatives, when not to use it, or names any sibling tool as a fallback. The usage context is inferred rather than spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
favorite_photosAIdempotent
Heart photos in Photos, or remove the heart.
Args: refs: Photo uuids or exact filenames. favorite: False to un-favorite.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | ||
| favorite | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only, non-destructive, idempotent behavior. The description adds the key context that the tool can both add and remove a favorite state, which is useful. However, it does not disclose error behavior, permission requirements, or effects on non-existent refs.
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 extremely concise, with no filler. The args section is clearly formatted and each line adds value. It earns its place entirely.
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 small 2-parameter tool with an output schema and rich annotations, the description covers the basics. However, it lacks any usage context or error/edge-case information, and does not reference sibling tools, which could leave an agent uncertain about the best tool for a given request.
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 description coverage is 0%, so the description must compensate. It explains that 'refs' accepts photo UUIDs or exact filenames, and that 'favorite: False' un-favorites. This is essential semantic detail not present in the schema, though filenames could be more specific (e.g., extension format).
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 action ('Heart photos') and its inverse ('remove the heart'), with a specific resource ('Photos'). It is easily distinguished from sibling tools like set_photo_title or add_to_album.
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?
No guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or exclusion conditions. The description only explains what the tool does, not when to choose it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
library_statsARead-onlyIdempotent
Size and shape of the library: totals, albums, named people, how much is indexed, and how much lives only in iCloud.
| 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?
The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool's safety profile is well established. The description adds useful context about what metrics are covered but does not disclose any additional behavioral traits such as response size, potential latency, or how the library is determined. With annotations covering the core behavior, a score of 3 is appropriate.
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 sentence that packs specific, useful details into a compact statement. Every clause contributes meaning, and the structure is easy to parse. There is no redundancy or filler.
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?
With no parameters, strong annotations, and an output schema present, the description only needs to communicate what the tool reports, which it does comprehensively. It names the categories of information (totals, albums, named people, indexing, iCloud-only content) and leaves return format details to the output schema. Nothing essential is missing.
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 tool takes zero parameters, so the baseline for parameter semantics is 4. The description does not need to explain parameter meaning since there are none. The schema coverage is effectively 100% because the parameter list is empty, so nothing is left undocumented.
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 identifies the resource (the library) and the specific kind of information provided (totals, albums, named people, indexing status, iCloud-only content). However, it lacks an explicit verb like 'get' or 'retrieve', so it describes the subject matter rather than stating the action, making it slightly less actionable than an ideal definition.
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 no guidance on when to use this tool versus alternatives. While no sibling tool appears to perform the same function, the description does not state when this should be chosen, nor does it mention any prerequisites or context. It is left entirely to the agent to infer that this is the go-to for library-level statistics.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_vocabularyARead-onlyIdempotent
The visual words this library actually knows.
Apple's classifier has a closed vocabulary. When a search finds nothing, this is how to discover the word it does understand instead.
Args: starts_with: Only terms beginning with this prefix. limit: Maximum terms to return.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| starts_with | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the tool is clearly safe. The description adds context about the closed vocabulary, which helps explain the tool's purpose but does not detail response behavior or pagination. This is adequate given the strong 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 compact, front-loads the core purpose, provides a practical usage context, and then documents parameters in a clear block. Every sentence earns its place with no redundancy.
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?
Despite lacking output schema details in the description, the presence of an output schema means return format is not needed. The tool's purpose, usage trigger, and all parameter semantics are fully covered, making it complete for an agent to invoke correctly.
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 explicitly documents each parameter in the Args section: 'starts_with' is defined as a prefix filter and 'limit' as the maximum number of terms returned. Since schema description coverage is 0%, this full compensation is essential and well executed.
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 that the tool lists the visual words the library knows, tying it to Apple's closed vocabulary. It explicitly differentiates itself from search tools by positioning it as the way to discover valid words when a search returns nothing.
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 a specific use case: 'When a search finds nothing, this is how to discover the word it does understand instead.' This implies when to use it relative to search, but it does not enumerate alternative tools or explicit when-not-to-use conditions. Still, the guidance is clear enough for an agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
look_at_photosARead-onlyIdempotent
Actually look at photos: renders each one and returns it as an image.
Use this on the top few results of every search before answering a "find my photo of X" question. Works even for photos that live only in iCloud, because it reads Apple's own local thumbnails rather than the original.
Args: refs: Photo uuids from search results, or exact filenames. size: Longest edge in pixels (128-2048).
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes | ||
| size | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given the annotations already declare the operation read-only and idempotent, the description adds genuinely useful behavior: it renders thumbnails, returns an image, and works for iCloud-only photos by reading Apple's local thumbnail cache rather than the original. This tells the agent an important implementation detail beyond the structured hints.
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 compact: one functional summary, one targeted usage instruction, one implementation note, and two parameter definitions. Every sentence earns its place and the most important 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?
For a simple two-parameter rendering tool, the description covers what the tool does, when to use it, how it behaves for iCloud photos, and both parameter meanings. The only minor gap is that with no output schema it does not specify how multiple refs are packaged in the returned image output, though the workflow is still clear.
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 description coverage is 0%, and the description fully compensates by explaining that refs accepts photo UUIDs from search results or exact filenames, and that size is the longest edge in pixels with a 128-2048 range. This is exactly the meaning an agent needs and is not present in the 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?
The opening sentence names the specific action ('renders each one') and the resource (photos), and explicitly states the output is an image. This distinguishes it from search_photos and export_originals, which either locate or export originals rather than render thumbnails.
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 an explicit trigger: use on the top few results of every search before answering a 'find my photo of X' question. It does not explicitly mention when not to use it or name alternatives, but the workflow context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
photo_infoARead-onlyIdempotent
Everything known about specific photos: metadata, ML labels, place, albums, faces, and any text Apple read inside the image.
Args: refs: Photo uuids or exact filenames.
| Name | Required | Description | Default |
|---|---|---|---|
| refs | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is clear. The description adds little beyond the annotations, only hinting at the breadth of data ('everything known'), but doesn't disclose potential privacy implications or rate limits. No contradiction.
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 two sentences: one functional summary and one parameter explanation. No wasted words, and the key action and resource 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?
For a simple single-parameter read-only tool with rich annotations and an output schema (not shown), the description covers what the tool returns and how to specify the target photo. It lacks guidance on when to choose this over siblings, but overall it is sufficiently complete for an agent to invoke correctly.
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%, so the description must carry the burden. It does by explaining that 'refs' are 'Photo uuids or exact filenames', which is far more informative than the schema's bare 'Refs' title. This effectively compensates for the lack of schema descriptions.
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 uses a specific verb 'Everything known' and lists the exact kinds of information (metadata, ML labels, place, albums, faces, text), making it obvious that this tool retrieves comprehensive metadata for a given photo. It is clearly distinct from siblings like search_photos or look_at_photos.
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 states what the tool does but does not explicitly say when to use it over alternatives. It implies usage for retrieving detailed photo information, but there is no mention of exclusions or comparisons to look_at_photos/edit tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_photosARead-onlyIdempotent
Search your entire Apple Photos library using Apple's own on-device index.
Natural visual phrases work best: 'sunset beach', 'receipt', 'dinner in Stockholm', 'whiteboard'. Filters narrow before ranking.
Args: query: What to find. Leave empty to browse by filter alone. limit: Maximum results (1-100). kind: 'photo' or 'video'. person: Only photos with this named face. album: Only photos in albums whose name contains this. place: Only photos taken somewhere matching this. year: Only photos from this calendar year. date_from: ISO date, inclusive lower bound (YYYY-MM-DD). date_to: ISO date, inclusive upper bound (YYYY-MM-DD). favorites_only: Only items hearted in Photos. screenshots: 'include', 'exclude', or 'only'. include_hidden: Include items in the Hidden album.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | ||
| year | No | ||
| album | No | ||
| limit | No | ||
| place | No | ||
| query | No | ||
| person | No | ||
| date_to | No | ||
| date_from | No | ||
| screenshots | No | include | |
| favorites_only | No | ||
| include_hidden | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond annotations: that the search leverages an on-device index, that filters narrow before ranking, and that natural language phrases work best. This gives the agent a useful model of how the tool behaves internally.
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 efficiently structured: a two-sentence overview followed by a terse Args list. Each line is informative and jargon-free, with no redundant fluff. It earns every word while remaining skimmable.
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?
Despite having 12 parameters and no schema descriptions, the description covers all parameters, provides usage examples, and specifies search behavior. Since an output schema exists, return values need no elaboration. The tool is fully specified 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.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description carries the full burden of parameter documentation. It defines every parameter in plain language, including formats (ISO date, inclusive bounds), allowed values (e.g., screenshots: include/exclude/only), and default behavior (query empty browses by filter). This is exemplary compensation for missing schema descriptions.
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?
Description opens with a specific verb and resource ('Search your entire Apple Photos library') and notes the use of Apple's on-device index. This clearly distinguishes it from sibling tools like look_at_photos or library_stats, which serve different purposes.
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 concrete usage context: recommends natural visual phrases, explains that filters narrow before ranking, and even describes browsing behavior when query is empty. It does not explicitly exclude alternatives, but the guidance is clear enough for correct selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
set_photo_descriptionCIdempotent
Set one photo's description/caption.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| description | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate this is a non-readonly, non-destructive, idempotent operation. The description does not add behavioral context such as permission requirements or whether the existing description gets replaced. No contradiction with annotations exists.
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 concise sentence that directly states the action. It is appropriately sized for a simple tool, though it omits useful details. The structure is straightforward and 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?
For a simple tool with an output schema, the description is minimal but misses key parameter semantics, particularly what 'ref' refers to. The agent may struggle to know how to fill the required parameters correctly. The lack of usage context further reduces completeness.
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?
With 0% schema description coverage, the description needed to explain the 'ref' and 'description' parameters, but it only mentions caption. The meaning of 'ref' (likely a photo identifier) is left undocumented. Therefore the description does not compensate for the schema's lack of detail.
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 uses a specific verb 'Set' with the resource 'one photo's description/caption', making the operation clear. It is implicitly distinguished from sibling tool set_photo_title which handles titles, though it doesn't explicitly name the alternative.
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?
No guidance is given about when to use this tool versus alternatives like set_photo_title or add_keywords. The description only states the action, leaving the agent to infer usage context. Since there are no exclusions or conditions, this is below average.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
set_photo_titleBIdempotent
Set one photo's title.
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| title | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already carry the safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=true), and the description states a mutation ('Set') that is consistent with them. It adds the single-photo scope beyond what annotations convey, but discloses no additional behavioral context such as overwriting behavior, auth requirements, or response characteristics. No contradiction with 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?
A single five-word sentence carries the entire description with absolute economy. Every word earns its place and there is zero filler, making it optimally scannable.
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?
The tool is simple (2 required scalar params, annotations provided, output schema present) and the description covers the basic purpose, so nothing critical is missing. However, the ref parameter is undefined beyond being a photo reference, and there is no sibling differentiation — gaps that matter given 0% schema description coverage.
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 description coverage is 0%, so the description must compensate. 'one photo's title' meaningfully maps title to the new title text and ref to the target photo, but ref remains vague — no format, type of identifier, or example is given. The explanation adds some value but does not fully resolve parameter ambiguity.
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 'Set one photo's title' names a specific verb (set), resource (a photo), and attribute (title), and adds the scope delimiter 'one photo.' It is clear about what the tool does, and the title-vs-description distinction from sibling set_photo_description is implicit, though it never names or contrasts the sibling explicitly.
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 implies usage only through the tool's name and one-line summary; it gives no explicit when-to-use, when-not-to-use, or alternative guidance. With near-identical sibling set_photo_description available, an agent gets no help distinguishing which tool to pick.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Each tool has a clearly distinct purpose: searching, viewing, metadata retrieval, stats, vocabulary, export, diagnostics, and distinct write operations (favorite, title, description, keywords, album, archive). No two tools perform the same action on the same resource.
Most tools follow a verb_noun pattern (search_photos, export_originals, add_to_album, set_photo_title). Minor deviations like photo_info (noun_noun) and doctor (single verb) are understandable but slightly break the uniform pattern.
13 tools is well within the ideal range for a domain-specific server. Each tool covers a meaningful capability without redundancy, making the set feel intentional and manageable.
The surface covers the full lifecycle of interacting with a photo library: searching, examining, editing metadata, organizing into albums, exporting originals, and archiving. The only missing operation—permanent deletion—is explicitly documented as impossible via scripting, making the completeness appropriate for the platform constraints.
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If you are the server author, to access and configure the admin panel.
Related MCP Connectors
Let ChatGPT, Claude & Cursor use your Mac: email, calendar, iMessage, Teams, files. Local, free.
Search, read, and write your Apple Notes from ChatGPT/Claude via a local Mac agent + MCP relay.
Personal asset search engine: everything you make or upload is searchable by what's inside it.
MCP connector that lets ChatGPT list, search, and run your Apple Shortcuts via a local Mac agent
Related MCP Servers
- FlicenseNot gradedqualityCmaintenanceEnables AI assistants to search, browse, and retrieve metadata and images from your Google Photos library. It supports content-based filtering, album listing, and location extraction via STDIO and HTTP transports.39
- FlicenseNot gradedqualityDmaintenanceAllows LLMs to access, scan, and search photos on connected iOS devices via USB using libimobiledevice and ChromaDB. It supports semantic search, metadata filtering, and automated file organization for efficient media management.
- AlicenseAqualityAmaintenanceEnables AI assistants to query and export from the macOS Apple Photos library using natural language, backed by osxphotos.2110213MIT

CoreViz MCPofficial
AlicenseNot gradedqualityDmaintenanceExposes a visual library with semantic search, tagging, editing, and management of photos as tools for AI agents like Claude Code.3048MIT
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