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.
Available components
Last scanned (3 hours ago)
Scores higher than 82% of MCPB servers
Category AI and Machine Learning
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. How we score → Why this is hard to score →
Supply Chain Security13
- 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.Unverified
- 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).Unverified
- No install/post-install scripts declared.Pass
- 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).Unverified
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 3 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability70
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 191 tokens (~95/item across 2 items; 2 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management60
- Stability observed for 18 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage92
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 75% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 2 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 3 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 4 Nov 26 +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.
- 2 Nov 26 +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.
- 1 Nov 26 0
- Stability: 0.43 → unverified ▼ security
- 31 Oct 26 +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.
- 29 Oct 26 +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.
- 26 Oct 26 +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.
- 24 Oct 26 +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.
- 22 Oct 26 +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.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed mcpb/https://github.com/markksantos/ai-model-watch/releases/download/v1.2.0/ai-model-watch-1.2.0.mcpb
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | mcpb |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
check_new_models Check for new AI models ~81
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.
| Name | Type | Req | Description |
|---|---|---|---|
| providers | array | – | Providers to check (default: all). |
No output schema declared.
No examples provided.
list_models List a provider's models ~78
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.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | integer | – | Maximum models to return (default 200). |
| provider | string | yes | – |
| search | string | – | Case-insensitive substring to match in the model ID or name. |
No output schema declared.
No examples provided.
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.
mcpb · ai-model-watch-1.2.0.mcpb
Download bundleCheck the download matches the SHA-256 the MCP registry declares before you open it. If it does not match, do not open the file.
SHA-256
4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274
macOS / Linux
echo "4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274 ai-model-watch-1.2.0.mcpb" | shasum -a 256 -c
Windows (PowerShell)
(Get-FileHash -Algorithm SHA256 .\ai-model-watch-1.2.0.mcpb).Hash -eq "4217ff4958f3277ebfe9be284b49c41402c86403b877a583c7d31a1d3f3d4274"
Run it in the folder you saved the bundle to, then open the file with an MCPB-capable host such as Claude Desktop.
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.