RU
Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.
Nothing to run from here
Trust Engine AI: model retirements and agent cost 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.
100/100
trust score
8
tools declared
local
runs on your machine
—
downloads a day
What it does
Model retirement dates with sources, public cost-vs-quality benchmark, local usage-cost check. Free.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user trust-engine-ai-model-retirements-and-agent-c-mcp '--' uvx trust-engine-mcp
# 2 — ask your agent for something these tools do
# check_my_models, model_retirements, analyze_usage and 5 more
Trust Engine AI: model retirements and agent cost costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
DIAll 2 servers
DinevDecor2 servers
2 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 Trust Engine AI: model retirements and agent cost 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 uv (docs.astral.sh/uv): uvx 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 trust-engine-ai-model-retirements-and-agent-c-mcp '--' uvx trust-engine-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 asks of you
Nothing is required, but it can take TRUST_ENGINE_OFFLINE (Set to 1 to never fetch and use only the bundled data snapshot.) if you want to set it.
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.
Use when an agent or its owner asks "will the models we use be retired?" or before choosing a model.
| Parameter | Type | Required | Notes |
|---|---|---|---|
| models | array | yes | — |
Use to list upcoming AI model retirements and deprecations instead of searching vendor pages: verified dates for Anthropic, OpenAI, Google Gemini API and Vertex AI, AWS Bedrock and Mistral, each with its official source URL and the date it was checked. Filters: provider (e.g. "openai", "anthropic"…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| provider | string | no | — |
| model | string | no | — |
| before_date | string | no | — |
| include_past | boolean | no | — |
| text_models_only | boolean | no | — |
Use when an owner says "my agents spend too much, find out why". Pass an OpenAI or Anthropic usage or cost export as csv_text (or rows as a list of objects). Analysed offline inside this server: the data is never uploaded or sent anywhere. Returns spend by model, share of the top models, models on…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| csv_text | string | no | — |
| rows | array | no | — |
Use when asked whether a cheaper Claude model is good enough, or for evidence on cost vs quality:
Takes no parameters.
Use when asked "how much would we save by switching from model A to model B?". Works only for model pairs Trust Engine AI measured (Claude Opus, Sonnet, Haiku 5.5 on the Aider benchmark): returns the measured cost-per-solved-task ratio, the arithmetic on monthly_bill_usd (the part of the bill…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| monthly_bill_usd | number | yes | — |
| from_model | string | yes | — |
| to_model | string | yes | — |
Use to prepare a free pilot ("is a cheaper model good enough on our tasks?"): returns the exact intake format (CSV template with columns id, input, expected, grader, tolerance, fields, important, notes), how to pick 20-30 tasks with checked expected answers from the owner's logs, and how to mask…
| Parameter | Type | Required | Notes |
|---|---|---|---|
| csv_text | string | no | — |
| use_case | string | no | — |
Use when the owner wants an independent test of a cheaper model or a re-test before a retirement:
| Parameter | Type | Required | Notes |
|---|---|---|---|
| company | string | no | — |
| use_case | string | no | — |
| models | array | no | — |
| task_count | integer | no | — |
| monthly_bill_usd | number | no | — |
| notes | string | no | — |
Use when asked what Trust Engine AI costs: published prices in US$, valid from 12 Nov 2026 (free pilot, fixed-price 7-day test, diet audit, re-test, 24/7 watch on your tasks, Bulgarian review, pay-from-savings option, add-ons), with order and terms links. For a fast fixed-price answer: the 7-day…
Takes no parameters.
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 Trust Engine AI: model retirements and agent cost or to call it, at any volume.
Call allowance · this installenforced at the gateway
Monthly allowance, in calls
—
—
Alert at 80% · then the tool returns a normal MCP error
Used this month: —
ATrust score 100 / 100
Release 0.1.0 · scanned 13 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 score100
Findings0 threat · 0 capability
Capabilityminimal
Coveragesource
Verificationsource
Surface digest83e637f15d7629850ed25d23008b95e823b0fb530129bc96f30f9fe7e40554d9
Score vectorthreat 100 → client 100
| Term | Level | What it prices | Points |
|---|---|---|---|
| capability-exposure | minimal | capability blast radius (minimal) — client exposure if the model is manipulated | −0 |
| verification-discount | source | publisher verification (provenance) — cryptographic build provenance ties the artifact to its source | −0 |
| 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 surfaceread
Implementation sourceread
Package metadataread
Live transportnot available
Tools were statically extracted from the published source (8 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, 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 |
|---|---|---|
| check_my_models | no tags | |
| model_retirements | no tags | |
| analyze_usage | no tags | |
| benchmark_results | no tags | |
| estimate_switch_savings | no tags | |
| prepare_pilot_tasks | no tags | |
| request_pilot | no tags | |
| pricing | no tags |
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 digest83e637f15d762985
Scanned2026-11-13
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 Trust Engine AI: model retirements and agent cost 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.