imladris
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@imladris Convene the council to analyze the pros and cons of universal basic income"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Imladris
Named for Imladris (Rivendell), seat of the Council of Elrond — where representatives of every free people gathered to weigh a hard decision together.

Design (source of truth)
docs/architecture/ — overview, pipeline, MCP surface,
configuration reference, provider abstraction, judge, security, and deployment.
Related MCP server: LLM Council MCP
How it works (one paragraph)
The harness's native model calls the imladris MCP tool (via the /council
or /council-session prompt). The server fans the prompt out to the configured panel (1–8
members) concurrently (each provider is a plain HTTPS call), collects answers
(with partial-result tolerance), then runs an API-side analysis judge
(consensus / contradictions / partial coverage / unique insights / blind spots).
The tool returns that analysis plus all raw panel answers. The native model reads
both and authors the final answer. A session call uses a local
~/.imladris/sessions/<thread_id>.json store to rebuild prior user/assistant turns for
the next panel pass. An MCP server can't call back into the
harness model, so the judge is API-side and the native model is always the outer
author.
Install
Bootstrap uv and install the imladris console
tool with a single command. You do not need a local checkout of this repository:
# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/FeanorsCodeSL/imladris/main/scripts/install.sh | sh# Windows
irm https://raw.githubusercontent.com/FeanorsCodeSL/imladris/main/scripts/install.ps1 | iexEach script installs uv if absent, resolves or installs Python 3.13, removes any
previous imladris uv tool environment, reinstalls with the resolved Python
interpreter, and verifies imladris --help before finishing. This puts two
commands on your console PATH: imladris (the configurator TUI) and
imladris-mcp (the MCP stdio server harnesses spawn).
Until a PyPI release is uploaded, this git-URL install requires git on PATH;
the installer checks that up front.
Use the installer as the supported install workflow, including for local source
testing. Raw uv tool install . is only useful when deliberately debugging uv
itself because it bypasses the installer safeguards.
Then run the configurator:
imladrisThe full-screen configurator loads the current config, shows the council roster,
lets you add/edit/delete members, assign the judge role, writes
~/.imladris/config.json + ~/.imladris/.env (0600), and wires selected
harnesses (Claude Code / Codex / OpenCode) with the imladris MCP entry. After
that, continue in your harness and invoke /council, /council-session, or the
imladris tool when you want deliberation.
Run imladris doctor to print the resolved roster, roles, and wired harnesses
(no secrets). Use imladris refresh-catalog or the TUI refresh action to update
the local models.dev cache; runtime deliberation only reads cache/seed data.
Council sessions are local files under ~/.imladris/sessions/. Clear one or all
of them with imladris clear-sessions [thread_id] or the imladris_clear_sessions
MCP tool.
Repository layout
orchestrator/ the service package
mcp_server.py FastMCP server — tools: imladris, imladris_status, imladris_clear_sessions; council + council-session prompts; main()
panel.py panel fan-out orchestration (partial results + degradation)
judge.py API-side analysis judge
model_catalog.py offline-first model metadata, models.dev parser, cache helpers
budget.py advisory context-budget estimates
sessions.py local council-session store and message reconstruction
providers/ single OpenAI-compatible chat provider kind + factory
settings.py JSON/YAML + env config loading/validation; roles panel/judge
models.py Pydantic request/response models
interfaces.py Protocol seams; fakes.py — deterministic test doubles
costing.py cost estimation; json_utils.py — tolerant judge JSON
cli/ imladris configurator: TUI, catalog, config ops, secrets, probe, harness/
tests/ pytest suite (respx for HTTP)
config/ imladris.example.yaml (illustrative providers, presets, pricing)
scripts/ install.sh, install.ps1, stdio smoke (Bash + PowerShell)
docs/architecture/ live architecture, configuration, security, deployment references
.github/workflows/ CI, sonar
.agents/skills/ imladris-deliberate skillDevelop
Requires Python 3.13.
python -m venv .venv
./.venv/bin/python -m pip install -r orchestrator/requirements-dev.txt
./.venv/bin/ruff check orchestrator # lint
./.venv/bin/ruff format --check orchestrator # style gate (CI enforces)
./.venv/bin/python -m pytest # tests
./.venv/bin/python -m orchestrator.mcp_server # run the MCP server over stdio
IMLADRIS_SOURCE="$(pwd)" ./scripts/install.sh # install/test from this checkoutOn Windows, use the same installer path:
.\scripts\install.ps1 -Source .Configuration
Run imladris to write ~/.imladris/config.json and ~/.imladris/.env (0600).
The in-repo config/imladris.example.yaml is illustrative only. Both JSON and
YAML config files are supported by extension; the TUI writes JSON.
Secrets live only in env vars referenced by name (api_key_env) from the
config — never in the config file, never logged, never returned by
imladris_status.
Resolution: --config <path> → IMLADRIS_CONFIG → ./imladris.{json,yaml} →
~/.imladris/config.{json,yaml}.
Integration
Per-harness setup (Claude Code, Codex, OpenCode) is wired automatically by the
imladris configurator. Every entry launches imladris-mcp over stdio with
IMLADRIS_CONFIG pointed at ~/.imladris/config.json. For the manual config
snippets, see docs/architecture/06-deployment.md.
License
MIT. See also SECURITY.md, CONTRIBUTING.md, THIRD-PARTY-NOTICES.md.
Available Tools
3 toolsimladrisA
Convene a panel of independent AI models to deliberate on a hard question, then return a structured analysis (consensus, disagreements, gaps, unique insights, blind spots) plus all raw panel answers. Use for research, expert critique, "compare and contrast", architecture/design trade-offs, or any task where being wrong is costly. You (the calling model) remain the final author: read the analysis + raw answers and write the answer.
Inputs: prompt (required), context, thread_id, prior_answer, +7 more. Returns: object
imladris_clear_sessionsA
Clear local Imladris council-session history.
Pass a thread_id to clear one session, or omit it to clear all local session JSON files under ~/.imladris/sessions.
Inputs: thread_id. Returns: object
imladris_statusA
Return resolved non-secret config: presets, provider roles, per-provider egress (on-prem/off-prem), and advisory context-budget state. Performs no liveness probe.
Never returns secret values or api_key_env names (plan §6.2).
Inputs: no named parameters. Returns: object
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 3 tools
Each tool serves a distinct purpose: the main tool for convening panels, clear_sessions for session cleanup, and status for configuration. No overlap in functionality.
The main tool is named simply 'imladris' while the auxiliary tools follow a 'imladris_verb_noun' pattern. This inconsistency breaks the pattern and could cause confusion.
Three tools is minimal but reasonable for a focused server covering core functionality, session management, and status. Slightly thin but not insufficient.
Missing operations for listing past sessions or retrieving cached results. The clear_sessions tool only removes sessions without providing overview or retrieval capabilities.
Maintenance
Related MCP Connectors
Convene a panel of expert AI personas to debate any decision from every side.
Put a decision to a council of top AI models and get one brief: verdict, splits, risks, next steps.
Multi-LLM council: 25+ frontier models in parallel, consensus scoring, verdict-first code review.
Commission a multi-model AI spec committee from your agent; get rubric-scored, build-ready specs.
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- AlicenseAqualityDmaintenanceEnables AI assistants to create council hearings where multiple LLMs (Claude, GPT, Gemini, Grok) debate topics and synthesize verdicts with trust scores and diverse perspectives.26 npmApache 2.0
- FlicenseNot gradedqualityBmaintenanceEnables AI coding agents to consult a multi-model deliberation council before irreversible, high-stakes decisions, returning structured architectural decision records with consensus and dissenting risks.1-
- AlicenseNot gradedqualityBmaintenanceEnables orchestrating a panel of frontier AI models that independently propose, critique, revise, and converge on a single agreed answer to a problem, using existing vendor subscriptions or API keys.19 npm1MIT
- AlicenseAqualityCmaintenanceEnables a structured multi-perspective deliberation workflow where three independent advisors propose strategies, cross-examine one another, and a final arbiter converges on a verdict, with all generation performed by the host agent.3MIT