io.github.CloudCorpRecords/modelpulse
OCI · GHCR.IO/CLOUDCORPRECORDS/MODELPULSE · 2 COMPONENTS
AI model releases, price changes, comparisons and cost estimates in one normalised feed.
Available components
Last scanned (4 hours ago)
Scores higher than 46% of OCI 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 Security0
- 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: this artifact ships no SBOM or dependency manifest, so there is no dependency list to read.Unverified
- Install-script risk not yet assessed.Unverified
- Dependency health could not be checked: this artifact ships no SBOM or dependency manifest, so there is no dependency list to read.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 0 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability77
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 1030 tokens (~128/item across 8 items; 8 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management3
- Stability observed for 1 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 8 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 8 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
Unverified: 1 category
A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
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 41
First indexed and scored.
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 oci/ghcr.io/cloudcorprecords/modelpulse@0.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | oci |
| 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 →
compare_models ~135
Put two or more models side by side: price, context, benchmark scores per subject, and what each costs per month at a given volume. Use this instead of calling get_model repeatedly: it aligns the fields and marks which subjects a model has not been tested on, so a missing score is not read as a low one.
| Name | Type | Req | Description |
|---|---|---|---|
| ids | array | yes | 2-6 OpenRouter ids, e.g. ['anthropic/claude-opus-5','openai/gpt-5.2'] |
| monthlyMillionTokens | number | – | Volume for the cost estimate. Default 10 (10M tokens a month). |
No output schema declared.
No examples provided.
estimate_cost ~115
What one model costs per month at a given token volume, in USD and KRW. Token prices are per million and hard to reason about directly; this turns them into a monthly bill. Input and output are mixed 75/25 unless you pass your own split.
| Name | Type | Req | Description |
|---|---|---|---|
| id | string | yes | OpenRouter id or Hugging Face repo id |
| inputShare | number | – | 0-1, share of tokens that are input. Default 0.75 |
| monthlyMillionTokens | number | – | Default 10 |
No output schema declared.
No examples provided.
find_replacement ~102
What to switch to when a model is gone or you need a fallback. Ranked by closeness in measured performance, not by vendor or price: what you usually need to preserve first is the quality of the output. Candidates whose context window is less than half the original are excluded. Returns the score, price and context deltas so you can judge; we do not pick for you.
| Name | Type | Req | Description |
|---|---|---|---|
| id | string | yes | OpenRouter id or Hugging Face repo id |
No output schema declared.
No examples provided.
get_model ~79
Pricing, context length and catalog status for one model. Accepts an OpenRouter id (anthropic/claude-opus-5) or a Hugging Face repo id.
| Name | Type | Req | Description |
|---|---|---|---|
| id | string | yes | Model identifier: an OpenRouter id (e.g. anthropic/claude-opus-5) or a Hugging Face repo id. |
No output schema declared.
No examples provided.
get_price_history ~81
Price history for one model: when the price changed and to what. The public catalog only exposes the current price, so this answers 'was this cheaper last month?'. Each entry holds until the next one. History starts when AI Wave began recording, not when the model launched.
| Name | Type | Req | Description |
|---|---|---|---|
| id | string | yes | OpenRouter id or Hugging Face repo id |
No output schema declared.
No examples provided.
get_today ~77
Today in one call: the top 5 models, which ones climbed, whose pricing changed in the last 24h, and what was newly listed in the last 7 days. Use this instead of paging list_model_changes and re-deriving the summary: price changes are already collapsed to one per model, with the raw count kept.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_model_changes ~162
Changes across AI models: new releases, price changes, API changes and deprecations. Normalized from vendor release notes, Hugging Face, the OpenRouter catalog, GitHub and the LiteLLM price map, deduplicated per model, each marked official or pending review. Poll with `since` (unix seconds) and feed the returned `latest` back next time.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | 1-100, default 20 |
| model | string | – | Vendor slug, e.g. claude |
| since | number | – | Unix seconds. Only changes detected after this, oldest first. |
| type | string | – | Filter by change type: new_model, price_change, api_change, open_source_surge or deprecation. |
No output schema declared.
No examples provided.
search_models ~279
Shortlist models by budget, context and capability, ranked by measured performance. Use this to answer 'which model should I use for X': it returns benchmark scores alongside price so the trade-off is visible in one call. Sort by a subject (math, coding, science, reading) to find a model good at one thing.
| Name | Type | Req | Description |
|---|---|---|---|
| accepts | string | – | What the model must be able to take in, e.g. image for vision tasks. |
| limit | number | – | 1-50, default 10 |
| maxInputPrice | number | – | USD per 1M input tokens |
| minContext | number | – | Minimum context length the model must offer, in tokens. |
| minIntelligence | number | – | Lowest acceptable overall index. Leaders sit near 53; the median is 16. |
| mode | string | – | What the model does. Non-chat modes are priced in other units: check price.unit. |
| outputs | string | – | What the model produces. A model that accepts video but writes text is 'text', not 'video': filter on what you need made, not what it can read. |
| sortBy | string | – | Ranking basis. Default 'intelligence' (overall). 'buzz' is popularity, not skill. |
| vendor | string | – | OpenRouter namespace, e.g. anthropic |
No output schema declared.
No examples provided.
How do I install the io.github.CloudCorpRecords/modelpulse MCP server?
io.github.CloudCorpRecords/modelpulse runs locally as a container image, launched with docker run --rm -i ghcr.io/cloudcorprecords/modelpulse:0.1.0. Ready-made configuration for Claude, Cursor, VS Code, Codex and 3 more is on this page, copied from each client's own documentation.
oci · ghcr.io/cloudcorprecords/modelpulse
claude mcp add cloudcorprecords-modelpulse -- docker run --rm -i ghcr.io/cloudcorprecords/modelpulse:0.1.0
What is the io.github.CloudCorpRecords/modelpulse MCP server?
io.github.CloudCorpRecords/modelpulse is an MCP server listed in the public MCP registry as io.github.CloudCorpRecords/modelpulse. AI model releases, price changes, comparisons and cost estimates in one normalised feed. This page covers its container image (ghcr.io/cloudcorprecords/modelpulse).
Is the io.github.CloudCorpRecords/modelpulse MCP server safe to use?
io.github.CloudCorpRecords/modelpulse scores 41 out of 100 on VerifyMCP. 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 io.github.CloudCorpRecords/modelpulse MCP server expose?
io.github.CloudCorpRecords/modelpulse exposes 8 tools: list_model_changes, get_model, get_price_history, search_models, compare_models, and 3 more. Their descriptions and schemas cost roughly 1,030 tokens of context every time the server is loaded.
Is the io.github.CloudCorpRecords/modelpulse MCP server still maintained?
io.github.CloudCorpRecords/modelpulse is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.CloudCorpRecords/modelpulse MCP server under?
io.github.CloudCorpRecords/modelpulse declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.