consult-mcp-server 0.4.1

Multi-model panel orchestration engine with an MCP adapter. The engine (consult.*) is MCP-free and usable as a library; the MCP adapter (consult.mcp.*) is an optional extra.

consult-mcp-server

Get a second opinion from a parallel panel of LLMs — without bloating your agent's context window.

consult is an MCP server that lets your agent (Claude Desktop, Cursor, Claude Code, etc.) fan a single prompt out to many LLMs in parallel, then return either the synthesised answer or a manifest of structured ~200-token capsules — so panel breadth doesn't cost parent-context tokens.

┌────────────┐    consult tool call     ┌──────────────────┐    parallel    ┌──────────┐
│ Your agent │ ───────────────────────▶ │  consult-mcp     │ ─────────────▶ │ Claude   │
│ (Claude    │   "what's your take?"    │  (this server)   │                │ GPT      │
│  Desktop / │ ◀─────────────────────── │                  │ ◀───────────── │ Gemini   │
│  Cursor /  │  synthesis + manifest    │  capsules ~200t  │   capsules     │ Grok     │
│  …)        │                          │  + resources     │                │ DeepSeek │
└────────────┘                          └──────────────────┘                │ …        │
                                                                            └──────────┘

Why this exists

If your agent already calls claude once, you might wonder why you'd want to ask 8 more models the same question. Three reasons:

1. One pass, many perspectives. Different families catch different things. Anthropic finds different bugs than OpenAI; Gemini calls out different risks; DeepSeek often surfaces the contrarian take.
2. Cheap structured second opinion. The manifest's per-panellist capsule is ~200 tokens — your agent can synthesise it in-band without paying for another flagship round-trip.
3. No context-window bloat. Full panellist bodies live as MCP resources at consult://runs/<id>/responses/<slug>; your agent only fetches them when it needs depth.

Alternatives fall short: PAL consensus serialises calls (sum of latencies); multi_mcp parallelises but no escape hatch from server-side synth; skill-only fan-outs assembled by the LLM via bash are brittle (token traps, key handling, endpoint drift).

---

Install

Claude Desktop

Claude Desktop does not inherit your shell's PATH or environment variables — you must give it the absolute path to consult-mcp and declare API keys inside the env block.

Tip: run consult-doctor --config after install to print a ready-to-paste JSON block populated with the absolute binary path and whichever keys are present in your shell environment.

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "consult": {
      "command": "/Users/you/.local/bin/uvx",
      "args": ["--from", "consult-mcp-server[mcp]", "consult-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-…",
        "OPENAI_API_KEY": "sk-…",
        "GEMINI_API_KEY": "AIza…",
        "OPENROUTER_API_KEY": "sk-or-…"
      }
    }
  }
}

Restart Claude Desktop, then ask: "use the consult tool to ask 3 models which Python package manager I should use."

Cursor

Edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "consult": {
      "command": "/Users/you/.local/bin/uvx",
      "args": ["--from", "consult-mcp-server[mcp]", "consult-mcp"],
      "env": {
        "ANTHROPIC_API_KEY": "sk-ant-…",
        "OPENAI_API_KEY": "sk-…"
      }
    }
  }
}

Same caveat as Claude Desktop: absolute path to uvx, env keys in the block.

Claude Code CLI

claude mcp add consult -- uvx --from "consult-mcp-server[mcp]" consult-mcp

The CLI inherits your shell env, so the keys you already have in .env / your shell rc will be visible.

Docker

docker run -i --rm \
  -e ANTHROPIC_API_KEY -e OPENAI_API_KEY -e GEMINI_API_KEY -e OPENROUTER_API_KEY \
  -v ~/.consult:/home/consult/.consult \
  ghcr.io/irwin-r/consult-mcp-server:latest

Stdio in / stdio out, just like the local binary. Image published per release to GHCR (multi-stage Python 3.12-slim base, ~150MB).

Smithery

https://smithery.ai/server/consult-mcp-server

Smithery's hosted UI prompts for keys; the same smithery.yaml config-schema applies.

From source (development)

git clone https://github.com/irwin-r/consult-mcp-server
cd consult-mcp-server
uv venv
uv pip install -e ".[dev]"
cp .env.example .env   # fill in keys
uv run pytest -v

Verify the install

consult-doctor          # offline: config + paths + key presence
consult-doctor --ping   # also fires a 1-token call per provider (~$0.0001)
consult-doctor --config # print copy-paste-ready MCP client JSON

---

The five tools

Tool	What it does	Use when
consult	Parallel panel + server-side synthesis. Hero.	"Just give me the answer."
panel	Parallel panel, returns raw manifest (no synth).	You want to synthesise yourself.
refine	Iterative consortium with arbiter scoring (≤3 rounds).	High-stakes; disagreement-heavy.
sequence	Chained multi-step where step N depends on N-1.	Decompose-then-answer; plan-then-execute.
synthesise	Re-collapse an existing run via a flagship model.	Different rubric/synthesiser on a prior run_id.

Tool descriptions are intentionally written as prompts for the calling agent (verb-first, explicit "use when…/don't use for…") so the agent reliably picks the right one without you having to spell it out.

---

Tiers & cost

Aliases are <family>-<tier> — version-neutral. The registry maps each alias to the current best model; the resolved LiteLLM ID is captured per run in registry_snapshot.json for reproducibility.

Tier	Models	Typical run cost	Use
nano (3)	claude-haiku, gemini-flash, gpt-nano	< $0.01	smoke tests / trivia
quick (5)	claude-haiku, gemini-pro, grok, qwen-max, kimi	~$0.05	snap second opinions
standard (10)	opus, sonnet, gpt-pro, gpt, gemini-pro, grok, qwen-max, kimi, glm, llama	$0.30–0.60	normal decisions
wide (10)	as standard, openrouter-routed where possible	$0.20–0.50	maximum diversity
deep (14)	standard + mistral, deepseek, mimo, sonar-pro	$0.50–1.00	high-stakes, includes web search
code (5)	opus, gpt-codex, gpt-mini, gemini-pro, deepseek	$0.20–0.40	code-heavy questions
review (6)	opus, gpt-codex, gpt-pro, gemini-pro, deepseek, grok	$0.30–0.60	PR / code review

A per-run cap (max_run_usd, default $5.00) refuses panels whose estimated cost exceeds the limit before any provider is called.

Custom models and overrides

Drop a ~/.consult/models.json (and/or stances.json) containing only what differs; it deep-merges over the packaged config. Add a model by declaring just its entry, override a single field of a packaged model by naming only that field, or remove a packaged entry by setting it to JSON null:

{
  "models": {
    "my-local": { "litellm_id": "ollama/llama3", "provider": "ollama" },
    "deepseek": null
  }
}

Config is cached for the process lifetime; restart the server after edits.

---

The manifest capsule

Each panellist returns a ~200-token structured extract (decision shape shown below; review and research kinds also supported):

{
  "slug": "claude-opus-1",
  "model_id": "anthropic/claude-opus-4-7",
  "status": "OK",
  "capsule": {
    "kind": "decision",
    "position": "supports B with caveats",
    "recommendation": "Use B with fallback to A",
    "key_points": ["…"],
    "unique_claims": ["Only model to flag cold-start regression"],
    "caveats": ["Assumes >100 RPS steady-state"],
    "confidence": 0.85
  },
  "resource_uri": "consult://runs/abc/responses/claude-opus-1",
  "latency_ms": 3420,
  "cost_usd": 0.04
}

Your agent can synthesise from this alone in most cases. Read the full body via the resource URI only when depth is needed.

---

Quickstart

After installing, from any connected agent:

> consult: prompt="Polars vs DuckDB for a 10GB Parquet timeseries?", tier="code"

Returns {run_id, synthesis, manifest, cost_usd, synthesiser}. The synthesis is markdown, ready to drop into your conversation.

For iterative consensus:

> refine:
    prompt="Should we migrate from REST to gRPC for the internal mesh?",
    models=[{model:"claude-opus"},{model:"gpt-pro"},{model:"gemini-pro"},{model:"deepseek"}],
    threshold=0.85

For chained reasoning:

> sequence:
    prompts=[
      "Decompose 'how should we scale our event pipeline?' into 4 sub-questions",
      "Answer sub-question 1: throughput requirements",
      "Answer sub-question 2: ordering guarantees",
      "Synthesise the final recommendation across the prior steps"
    ],
    models=[{model:"claude-opus"},{model:"gpt-pro"}]

---

End-to-end walkthrough

A full tour. Assumes the install above and at least one provider key in .env.

1. Smoke-test the install (no API spend)

.venv/bin/python -c "
import asyncio
from consult import panel, ModelSpec
async def go():
    h = await panel('hello', [ModelSpec(model='claude-haiku')], dry_run=True)
    print('partial:', h.partial, '| reason:', h.partial_reason)
asyncio.run(go())
"
# partial: True | reason: dry_run: estimated cost $0.0001

2. First real consult (~$0.20 on the code tier)

> consult: prompt="Polars vs DuckDB for 10GB Parquet timeseries?", tier=code

3. Inspect a panellist's full body

> read resource: consult://runs/<run_id>/responses/claude-opus-1

4. Tail progress in real time

tail -f ~/.consult/runs/<run_id>/_progress.log

Agents that send a progressToken get the same events as notifications/progress.

5. Follow-up via continuation_id

> refine: prompt="OK now what about Iceberg vs Delta on top of that?",
          continuation_id="<prior run_id>",
          models=[{model:"claude-opus"},{model:"deepseek"}]

The prior run's synthesis is prepended as "Prior consultation summary".

6. Stochastic averaging with model:N

> panel: models=[{model:"claude-haiku:3"},{model:"gpt-mini:3"}], prompt="…"

Six panellists total — three runs each of two cheap models.

A single panel is capped at 64 panellists (the :N counts plus any bare specs, summed). Set CONSULT_MAX_PANEL_SIZE to raise or lower it; a request over the cap is rejected before the run starts. The default leaves room for the largest built-in tier (deep, 14) and generous averaging.

7. Check today's spend

consult-ledger today
# {"date":"2026-05-21","total_usd":2.36,"total_known":false,"runs":[…]}

total_known: false means at least one panellist had pricing missing from the LiteLLM table.

8. View a run as a rich HTML page

consult-view <run_id>          # writes ~/.consult/runs/<run_id>/feed.html
consult-view <run_id> --open   # also opens in default browser

Self-contained HTML — header pills, prompt, synthesis (markdown), per-round arbiter verdicts (refine), per-panellist cards with capsule + full body, and a chronological timeline from _progress.log. No external assets, no JS.

---

Driving the engine without MCP

The engine package (consult.*) is MCP-free and reusable as a library:

from consult import consult, panel, refine, ModelSpec

# Hero tool
result = await consult("question?", tier="standard")
print(result.synthesis, result.cost_usd)

# Lower-level
handle = await panel("question?", [ModelSpec(model="claude-opus"), ModelSpec(model="gpt-pro")])

# Iterative
verdict = await refine(
    "tough decision?",
    [ModelSpec(model="claude-opus"), ModelSpec(model="deepseek")],
    threshold=0.85,
)

Swap the URI scheme for a non-MCP transport:

from consult import artifacts
artifacts.set_resource_uri_formatter(
    lambda run_id, slug: f"https://api.example.com/runs/{run_id}/{slug}"
)

---

Telemetry

Install the otel extra and point it at a collector to emit gen_ai.* OpenTelemetry spans, one per panellist call:

pip install "consult-mcp-server[otel]"
export OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317

Without the extra (or without the endpoint set) the telemetry helpers are no-ops at zero overhead.

Retention

Run artefacts under the runs directory accumulate indefinitely. consult-gc prunes them by age and/or count:

consult-gc --max-age-days 30           # drop runs older than 30 days
consult-gc --max-count 500             # keep only the newest 500 runs
consult-gc --dry-run --max-count 500   # show what would go, delete nothing

Defaults can be set with CONSULT_RUNS_RETENTION_DAYS and CONSULT_RUNS_MAX; the CLI flags override them.

Security

Read SECURITY.md for the full threat model. Short version:

• git_diff attachments must resolve under CONSULT_TRUSTED_REPO_ROOTS (defaults to the server's CWD). Symlinks are resolved before the check; escape attempts fail closed.
• File attachments are containment-checked only when CONSULT_TRUSTED_REPO_ROOTS is set. When it's unset, any path the server process can read is accepted, on the reasoning that the calling agent already has filesystem access of its own. If the server runs with broader filesystem access than the calling agent (Docker, a shared host, a remote deployment), set CONSULT_TRUSTED_REPO_ROOTS so attachments are confined to the directories you intend.
• Run artefacts are chmod 0o700 — per-run prompts (often containing pasted credentials or code) are not world-readable on shared hosts.
• git diff runs with global/system git config neutralised so a malicious .gitattributes filter can't execute.
• LiteLLM exception strings are scrubbed for sk-…, AIza…, Bearer …, x-api-key: and similar before they reach the manifest or the progress log. This covers provider error text only. The raw prompt and panellist responses are stored unredacted inside the 0o700 run directory, so treat that directory as sensitive.

Privacy note

The model registry tags each entry with a privacy_tier:

• first_party — direct API to Anthropic / OpenAI / Google.
• aggregator — routed via OpenRouter (Grok, Kimi, Qwen, DeepSeek, Llama, Mistral, GLM, MiMo, Sonar-Pro).

Mixing tiers in one panel broadcasts the same prompt to providers with different data-retention policies. For prompts containing sensitive material, prefer tier="standard" (mostly first-party) over tier="wide" or tier="deep" (heavily aggregator-routed).

---

Repo layout

consult/                # ENGINE — no mcp.* imports
  runner/               # async fan-out package (facade in __init__)
    transport.py        #   LiteLLM retry/streaming/Responses adapter
    fit.py              #   context-budget fitting + attachment trims
    specs.py            #   model:N expansion + slug grammar
    costs.py            #   panel cost estimation
    fanout.py           #   _call_one, slow-tail dropout, fanout()
  capsule.py            # post-fanout structured extraction
  synth.py              # flagship synthesiser
  refine.py             # arbiter-driven loop (max 3 rounds) + continuation
  sequence.py           # chained multi-step
  orchestrate.py        # consult() hero
  ledger.py             # daily cost ledger (consult-ledger)
  viewer.py             # static HTML run renderer (consult-view)
  doctor.py             # diagnostic CLI (consult-doctor)
  registry.py           # models.json + stances.json loader
  artifacts.py          # ~/.consult/runs/<id>/ layout + URI formatter
  attachments.py        # file/diff inlining + trusted-roots enforcement
  sources.py            # git_diff resolver (hardened subprocess)
  context.py            # per-run bundle + blinding
  progress.py           # typed ProgressEvent union
  status.py             # LiteLLM response → Status
  types.py              # Pydantic models (StrictModel base)
  jsonparse.py          # tolerant JSON extraction from model output
  cost.py               # CostMeter — spend roll-up with unknown propagation
  envutil.py            # tolerant numeric env parsing
  exceptions.py         # typed exception taxonomy
  provider_caps.py      # per-provider capability flags (temperature, etc.)
  cli_executor.py       # CLI-as-panellist subprocess transport
  telemetry.py          # optional OpenTelemetry spans (otel extra)
  peer_rank.py          # peer-ranking aggregation (opt-in, off by default)
  voting.py             # medoid selection over model:N capsules (opt-in)
  strategies.py         # refine elimination strategies (opt-in)
  task_store.py         # in-process registry for long-running task mode
  mcp/                  # MCP ADAPTER — only thing that imports mcp.*
    server.py, handlers.py, schemas.py, errors.py, __main__.py
  config/
    models.json         # registry with privacy_tier annotations
    stances.json        # persona prompts
    rubrics/            # synthesis rubric markdown (consensus, code_review, ...)
  schemas/
    capsule_v2.json     # JSON Schema for the capsule wire shape (reference)
tests/                  # pytest (offline + live, gated on keys)
.github/workflows/      # CI: ruff + pytest on Py 3.11/3.12/3.13
FRICTION.md             # internal dogfooding log (kept for transparency)
SECURITY.md             # threat model + disclosure path
CONTRIBUTING.md         # dev setup + style

---

Contributing

See CONTRIBUTING.md. Issues and PRs welcome; please open an issue first for non-trivial changes so we can agree on shape.

License

MIT. See LICENSE.