# AI Model Watch (mcpb · ai-model-watch-1.2.0.mcpb)

List the models each AI provider's API serves and see which are new since the last check.

- Trust score: 51/100 (low)
- Change this week: +3
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- mcpb · `ai-model-watch-1.2.0.mcpb`: 51/100 (this document), [markdown](https://verifymcp.io/servers/markksantos-ai-model-watch/ai-model-watch.md), [page](https://verifymcp.io/servers/markksantos-ai-model-watch/ai-model-watch)

## Channel facts

- Registry: `mcpb`
- Package: `https://github.com/markksantos/ai-model-watch/releases/download/v1.2.0/ai-model-watch-1.2.0.mcpb`
- Transport: `stdio`
- SHA-256: `4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-05.

- **Supply Chain Security**: 13/100
  - No malware scan is available for this kind of package: the supply-chain vendors we use do not cover it. This is a permanent gap in our coverage, not a finding about the package.
  - Known CVEs could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).
  - No install/post-install scripts declared.
  - Dependency health could not be checked: we found no dependency list to check in this bundle: either it runs a compiled binary, whose bundled manifests we do not read, or none of the package.json, requirements.txt, pyproject.toml or uv.lock files it ships for its runtime declares a dependency we can read (a requirements.txt that only points at a setup.py project, a local path, or a nested requirements file we cannot follow counts as none).
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 3 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 70/100
  - AI-judged instruction clarity (good).
  - Tool/resource definitions use about 191 tokens (~95/item across 2 items; 2 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 60/100
  - Stability observed for 18 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 92/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 75% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 2 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 3 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the AI Model Watch MCP server?

AI Model Watch ships as an MCPB bundle, a single file you download and open in an MCP host that supports MCPB bundles, such as Claude Desktop. The host reads the launch command from the bundle's own manifest, so there is no command to copy. Before opening it, check the file's SHA-256 matches the one the MCP registry declares for it, 4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274; the commands to do that are on this page. If it does not match, do not open it.

- Download bundle: `https://github.com/markksantos/ai-model-watch/releases/download/v1.2.0/ai-model-watch-1.2.0.mcpb`
- SHA-256: `4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274`

### Verify on macOS / Linux

```bash
echo "4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274  ai-model-watch-1.2.0.mcpb" | shasum -a 256 -c
```

### Verify on Windows (PowerShell)

```powershell
(Get-FileHash -Algorithm SHA256 .\ai-model-watch-1.2.0.mcpb).Hash -eq "4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274"
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-04 (score 51, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-02 (score 50, +1)

No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-01 (score 49, 0)

- [security regression] Stability: 0.43 → unverified

### 2026-10-31 (score 49, +1)

No change was recorded against any check on this day. Stability & Change Management went from 40 to 43. That category is still filling its 30-day observation window: 12 days of observed history at the previous scan, 13 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-29 (score 48, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-26 (score 47, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-24 (score 46, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-22 (score 45, +1)

No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.

## MCP tools (2)

### `check_new_models` (~81 tokens)

Check for new AI models

Fetch each provider's model list and report the model IDs that weren't there at the last check, then save the new lists (the same state file the CLI uses). The first check records a baseline. Providers without an API key in the server's environment are skipped; OpenRouter needs none.

Input parameters:

- `providers` (array): Providers to check (default: all).

### `list_models` (~78 tokens)

List a provider's models

List the model IDs one provider's API serves right now, optionally filtered by a substring. Read-only: it does not touch the saved state.

Input parameters:

- `limit` (integer): Maximum models to return (default 200).
- `provider` (string, required)
- `search` (string): Case-insensitive substring to match in the model ID or name.

## Diagnostics

Captured diagnostic sections: Provenance. The full working is on the page: https://verifymcp.io/servers/markksantos-ai-model-watch/ai-model-watch#diagnostics

## Score history

- 2026-11-05: 51
- 2026-11-04: 51
- 2026-11-03: 50
- 2026-11-02: 50
- 2026-11-01: 49
- 2026-10-31: 49
- 2026-10-30: 48
- 2026-10-29: 48
- 2026-10-28: 47
- 2026-10-27: 47
- 2026-10-26: 47
- 2026-10-25: 46
- 2026-10-24: 46
- 2026-10-23: 45
- 2026-10-22: 45
- 2026-10-21: 44
- 2026-10-20: 44
- 2026-10-19: 43
- 2026-10-18: 43

## Common questions

### What is the AI Model Watch MCP server?

AI Model Watch is an MCP server listed in the public MCP registry as io.github.markksantos/ai-model-watch. List the models each AI provider's API serves and see which are new since the last check. This page covers its MCPB bundle (https://github.com/markksantos/ai-model-watch/releases/download/v1.2.0/ai-model-watch-1.2.0.mcpb).

### Is the AI Model Watch MCP server safe to use?

AI Model Watch scores 51 out of 100 on VerifyMCP. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the AI Model Watch MCP server expose?

AI Model Watch exposes 2 tools: check_new_models, list_models. Their descriptions and schemas cost roughly 159 tokens of context every time the server is loaded.

### What licence is the AI Model Watch MCP server under?

AI Model Watch declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- Repository: https://github.com/markksantos/ai-model-watch
- Website: https://artificialwatch.com/mcp
- Changelog RSS feed: https://verifymcp.io/servers/markksantos-ai-model-watch/ai-model-watch.xml
- Changelog JSON feed: https://verifymcp.io/servers/markksantos-ai-model-watch/ai-model-watch.json
- HTML version of this page: https://verifymcp.io/servers/markksantos-ai-model-watch/ai-model-watch