MCP Server for Z.AI - A Model Context Protocol server that provides AI capabilities
Nothing to run from here
AETRE & The Governed Agent 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.
32
tools declared
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trust score
local
runs on your machine
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calls last 7d
What it does
Decision-theoretic triage and fail-closed agent gating: 32 tools over stdio.
Quickstart
# 1 — add it to Claude Code
claude mcp add --scope user aetre-the-governed-agent-mcp '--' docker run -i --rm ghcr.io/grayclayton/aetre-mcp:0.8.1
# 2 — ask your agent for something these tools do
# aetre_system_catalog, aetre_triage_proposal, aetre_calculate_voi and 29 more
AETRE & The Governed Agent costs nothing on mcprush: there is no plan to choose here, no cap to set and nothing mcprush can bill you for.
Publishernot claimed
GRAll 1 server
grayclayton1 server
1 category
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 AETRE & The Governed Agent 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. It has not been scanned here, so read the surface, and its source, 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 Docker installed and running. 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 aetre-the-governed-agent-mcp '--' docker run -i --rm ghcr.io/grayclayton/aetre-mcp:0.8.1Reconnect, 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 image: 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
32 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.
Comprehensive system introspection returning AETRE architecture, bundled synthetic fixtures, optional data adapters, connectors, mathematical tools, and institutional tiers.
No parameter schema on file.
Applies transparent, uncalibrated lexical routing indicators to proposal text, then calculates a VOI index and demonstration stage route (FAST-PASS, FAST-REJECT, or DEEP REVIEW). Not a validated estimate of scientific quality.
No parameter schema on file.
Calculates the exact Bayesian Value of Information (VOI) for crossing a top-K selection boundary under Gaussian conjugate updates.
No parameter schema on file.
Evaluates evaluator queue load using Kingman
No parameter schema on file.
Calculates the unbiased Horvitz-Thompson exploration audit estimator (H_hat_D) and 95% confidence intervals on deprioritized candidates to catch false negative breakthroughs.
No parameter schema on file.
Simulates submitter entry equilibrium under generative AI generation costs and refundable submission deposits to curb spam floods.
No parameter schema on file.
Calculates Proposition 1 theoretical recall ceiling R_N <= min(1, K_N / H_N) to determine if a pipeline is mathematically capacity-constrained.
No parameter schema on file.
Calculates Bayesian posterior mean and uncertainty under correlated multi-agent evaluator noise (rho_corr), preventing artificial overconfidence from redundant LLM outputs.
No parameter schema on file.
Calculates Generalized Pareto / Heavy-Tailed Value of Information (VOI) to optimize selection pipelines for positive black swan breakthrough discovery.
No parameter schema on file.
Calculates super-linear anti-sybil staking deposit requirements (Stake(m) = S_0 * m^gamma) to deter mass AI spam submissions while preserving human entry.
No parameter schema on file.
Evaluates a multi-specialist heterogeneous reviewer network, identifying bottleneck domains and generating capacity rebalancing actions.
No parameter schema on file.
Comprehensive pre-submission diagnostic scorecard for authors and researchers, calculating crowd novelty percentile, reviewer disagreement risk, and prescriptive refinement actions.
No parameter schema on file.
Runs a paired-cohort Monte Carlo simulation across all 4 screening regimes, comparing Quality Throughput, FDR, Unconventional Recall, and Human Reviews with central 95% run-to-run outcome intervals (not confidence intervals for the mean).
No parameter schema on file.
Batch applies disclosed, uncalibrated lexical indicators to a cohort, computing heuristic ranks, VOI ranks, and demonstration stream allocation (Stream A Fast-Reject, Stream B Deep Review, Stream C Fast-Pass).
No parameter schema on file.
Calculates the Proposition 1 theoretical recall decay curve across arrival expansion scales (e.g. 1x, 2x, 5x, 10x, 20x, 50x) demonstrating capacity collapse points.
No parameter schema on file.
Runs a multi-policy held-out review allocation backtest across 8 triage policies under fixed review budget K, evaluating true decision flips, precision, recall, and paired bootstrap intervals.
No parameter schema on file.
Fits Platt logistic scaling on continuous model scores and binary labels, returning slope, intercept, Expected Calibration Error (ECE), and Brier score.
No parameter schema on file.
Computes multi-attribute Bayesian Value of Information across orthogonal proposal evaluation dimensions (Novelty, Rigor, Impact, Feasibility), outputting composite VOI and optimal dimension-specific review targets.
No parameter schema on file.
Optimizes reviewer-to-proposal assignment by maximizing domain/keyword affinity while enforcing Kingman queue utilization constraints (rho <= 0.85) on individual reviewer workloads.
No parameter schema on file.
Calculates optimal dynamic Bayesian stopping boundaries for sequential reviews (Accept, Reject, or Solicit More Reviews) based on posterior decision confidence and boundary VOI.
No parameter schema on file.
Pillar I Bellman Governor: evaluates Bayesian dynamic programming stopping policy over multi-stage pass lattices under asymmetric loss stakes (L/R), returning optimal action (CONTINUE, HALT_AND_COMMIT, HALT_AND_REJECT), expected utility, VOI, and critical threshold p*.
No parameter schema on file.
Section 4.1 Tripartite Review Boundary: evaluates whether an autonomous coding candidate should be AUTO-admitted, sent to human REVIEW, or ABSTAINED based on reviewer effort cost and knapsack capacity shadow price lambda_K.
No parameter schema on file.
Pillar V Knapsack Queue Controller: packs candidate pull requests into the review queue under capacity budget K using the c-mu rule (density rho_i = E[U_i] / k_i) and calculates the dual capacity shadow price lambda_K.
No parameter schema on file.
Road A Tiered Verification Gate: evaluates host pre-checks (Tier 0 syntactic AST parsing and structural validation) on candidate Python code or pull request patches to short-circuit broken submissions before expensive container CI escalation.
No parameter schema on file.
Micro-level loss evaluation comparing autonomous execution against deferral to human review, under an asymmetric loss matrix weighted by irreversibility.
No parameter schema on file.
Solves the finite-horizon optimal stopping problem by backward induction: at each step the agent either takes the terminal payoff or pays the continuation cost for one more step of evidence.
No parameter schema on file.
Evaluates declarative safety invariants against a runtime state snapshot before a state change is committed, returning every violated rule rather than the first.
No parameter schema on file.
Computes the marginal shadow price of scarce reviewer time (lambda_K) from queue backlog and review capacity under a heavy-tailed utility distribution, and the factor by which it elevates the admission cutoff.
No parameter schema on file.
Fits the recall saturation curve Recall(K) = 1 - exp(-gamma K) to observed budget and recall pairs by least squares, returning the fitted gamma and the budget beyond which marginal recall stops paying for itself.
No parameter schema on file.
Generates a tamper-evident SHA-256 decision receipt over an execution
No parameter schema on file.
Runs the venture dealflow triage benchmark: generates a synthetic heavy-tailed cohort of deals and compares status-quo preliminary-score screening against AETRE heavy-tailed VOI triage under a fixed diligence budget.
No parameter schema on file.
Sweeps the submission fee across a range and returns the submitter equilibrium at each point, showing how entry volume and low-quality deterrence respond to the staking fee rather than evaluating a single fee.
No parameter schema on file.
Free
$0 forever
- Every tool, no call limit
- No card, and mcprush charges nothing for it
- Source published under AGPL-3.0
- 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 AETRE & The Governed Agent 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: —
—Grade not published
No rule-by-rule report on file for this release
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.
This listing ships as a container image. The scanner reads packages on npm and PyPI and public source; an image is neither, so there is no grade here — not a withheld one, an unmeasured one.
Release history
It installs from a container image rather than from a versioned package, so the command cannot be pinned to a release. The history below is for reading; the current release on file is 0.8.1.
0.8.1
undated
latestNo release note was published with this version.
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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 AETRE & The Governed Agent 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.