MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
Modelpulse is installed from its publisher's own source and answers where it runs, so this marketplace is not in the path of a single call. There is no address here to send one to, and a panel that pretended otherwise would be showing you an answer we made up. Install it and call it from your own client — the Installation tab has the entry for each one.
99/100
trust score
8
tools declared
local
runs on your machine
—
downloads a day
What it does
AI model releases, price changes, comparisons and cost estimates in one normalised feed.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user modelpulse-mcp '--' npx -y @reneturcios/modelpulse mcp
# 2 — ask your agent for something these tools do
# list_model_changes, get_model, get_price_history and 5 more
Modelpulse costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
CLAll 380 servers
CloudCorpRecords380 servers
28 categories
Collected from a public index. Nobody has claimed this account, so nothing here was written by its author — claim it if it is yours.
Installation
Where are you running it?
Every route below installs the same thing and ends at the same approval screen. This one runs on your machine: your client starts Modelpulse as a process under your own user, with your files and your network, so the tool surface below is what it can do to your computer rather than to a server somewhere else. Its scan result is on the Security tab — read the surface before you approve it.
Claude Codedeclared by this listing
This is a public server: you run it yourself and this marketplace is not in the path. You need Node.js — the current LTS (24) is the safe choice; npx comes with it. Claude Code registers it in one command; --scope user makes it available in every project. The '--' is in quotes so that PowerShell passes it on.
claude mcp add --scope user modelpulse-mcp '--' npx -y @reneturcios/modelpulse mcpReconnect, or start a new session, and the tools appear in the model’s tool list.
Free on mcprush, and it runs from its publisher’s own package: this marketplace is not in the path, so there is no key of ours to hold and no card to add.
What it writes
One config entry your client uses to start the process locally. A local server runs with your file system and your network, which is why it is priced without metering.
What you approve
8 tools, with what each one reads, writes and reaches shown before you agree — the same list on every route above. Read the tool surface.
Tool surface
What the model actually sees. Descriptions are diffed on every release — see version history.
Read from its source code. A server that builds its tool list when it starts can list more tools than this.
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…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| since | number | no | Unix seconds. Only changes detected after this, oldest first. |
| type | string | no | Filter by change type: new_model, price_change, api_change, open_source_surge or deprecation. |
| model | string | no | Vendor slug, e.g. claude |
| limit | number | no | 1-100, default 20 |
Pricing, context length and catalog status for one model. Accepts an OpenRouter id (anthropic/claude-opus-5) or a Hugging Face repo id.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| id | string | yes | Model identifier: an OpenRouter id (e.g. anthropic/claude-opus-5) or a Hugging Face repo id. |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| id | string | yes | OpenRouter id or Hugging Face repo id |
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…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| vendor | string | no | OpenRouter namespace, e.g. anthropic |
| maxInputPrice | number | no | USD per 1M input tokens |
| minContext | number | no | Minimum context length the model must offer, in tokens. |
| minIntelligence | number | no | Lowest acceptable overall index. Leaders sit near 53; the median is 16. |
| sortBy | string | no | Ranking basis. Default 'intelligence' (overall). 'buzz' is popularity, not skill. |
| outputs | string | no | 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. |
| mode | string | no | What the model does. Non-chat modes are priced in other units: check price.unit. |
| accepts | string | no | What the model must be able to take in, e.g. image for vision tasks. |
| limit | number | no | 1-50, default 10 |
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…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| ids | array | yes | 2-6 OpenRouter ids, e.g. ['anthropic/claude-opus-5','openai/gpt-5.2'] |
| monthlyMillionTokens | number | no | Volume for the cost estimate. Default 10 (10M tokens a month). |
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.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| id | string | yes | OpenRouter id or Hugging Face repo id |
| monthlyMillionTokens | number | no | Default 10 |
| inputShare | number | no | 0-1, share of tokens that are input. Default 0.75 |
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.
No parameter schema on file.
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,…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| id | string | yes | OpenRouter id or Hugging Face repo id |
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under MIT
- Runs on your machine — nothing of it reaches our gateway
- Nothing to cap, because mcprush bills nothing
What counts against your monthly calls
| Tool | Unit | Calls used | Out of the allowance |
|---|
Nothing here is billable: mcprush charges nothing to install Modelpulse or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
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—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 99 / 100
Release 0.1.0 · scanned 5 Nov 2026
Two independent axes, because powerful and malicious are different questions. The grade is threat only. The capability level is blast radius, and it is never a penalty on the grade — it is priced as one subtract-only term in the score, where you can see it.
Threat score100
Client score99
Findings0 threat · 0 capability
Capabilityminimal
Coveragesource
Verificationrepo
Surface digestb10bd2f29c487956c167906a25166a98fa64e78243b5b1d301022b338f21e640
Score vectorthreat 100 → client 99
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| verification-discount | repo | publisher verification (public source) — no provenance, but the source is public and inspectable | −1 |
| coverage-honesty | source | inspection depth (source) — how much of the target the scan could see | −0 |
score = clamp(round(threatScore − E_cap − E_ver − E_cov), 0, 100). No model, no opinion, no network call at scoring time: the same methodology version against the same bytes always produces this number, and every line above is a field of the report rather than a summary of it.
What the scan could actually read
A grade is only as meaningful as its coverage, so the scanner publishes its own depth before it publishes its result.
Tool surfacenot available
Implementation sourceread
Package metadataread
Live transportnot available
No tools were enumerated, so prompt-injection, capability and toxic-flow analysis had no tool surface to inspect. To grade a package’s real runtime tools, scan the running server: --command "npx -y <package>".
Capability — what it could do if the model were manipulated
Tags derived from each tool’s schema and the implementation, not from what the tool calls itself. minimal is the level these add up to.
| Tool | Capability tags | Why the tag was assigned |
|---|---|---|
| No per-tool rows for this release — see the note below. | ||
No tool surface was enumerated, so there is nothing to attribute a capability to.
Toxic-flow graph
The lethal trifecta, checked as a graph rather than as a checklist: untrusted input, a sensitive source and an external sink have to meet before there is a path worth worrying about.
The public result for this release does not print the flow graph, so there is nothing to show here. That is not the same as "no paths were found": what the scan did read is above, under coverage.
Supply chain and provenance
Integrityfirst-seen
Surface digestb10bd2f29c487956
Scanned2026-11-05
This is the first scan of this surface here, so there is nothing yet to compare it against.
Every result on this tab comes from one deterministic pass over the published package — offline, rule by rule, and auditable line by line above. Same methodology version, same bytes, same score.
Release history
Always latest — the install command names no version, so it installs the newest release when it runs; the current release on file is 0.1.0. Pin a release below and the command asks for exactly that one.
0.1.0
undated
latestNo release note was published with this version.
—
no reviews yet
Who can review
One review per account, from a signed-in account that does not publish this listing. Nothing else is asked — you can say what you think before you install it. Publishers can reply once per review, and a review can be edited or deleted by whoever wrote it.
Sign in to write one
Any signed-in account that does not publish Modelpulse can review it, once — before installing it or after. A review from an account that has installed it is marked as one.
No reviews yet
Nobody has reviewed this listing. The rating on the card is the mean of the reviews written here and nothing else, so there is no rating until somebody writes the first — which takes a signed-in account that does not publish it, and nothing else.