fabro/docs/public/reference/user-configuration.mdx
Ryan Neal d5b2220ed3
feat(llm): Amazon Bedrock provider — Converse codec, SigV4 + API-key auth (#459)
Adds **Amazon Bedrock** as an opt-in built-in provider, over Bedrock's
unified **Converse / ConverseStream** API. One codec serves every
Converse-capable family — Claude, Amazon Nova, Meta Llama, Mistral,
DeepSeek, Moonshot Kimi, Z.AI GLM, MiniMax, NVIDIA Nemotron, and OpenAI
gpt-oss — because AWS translates the envelope to each model's native
dialect server-side. Auth is either **AWS SigV4** (the default
credential chain — env / profile / IMDS / IRSA / SSO, resolved per
request so sessions refresh) or a **Bedrock API key**
(`AWS_BEARER_TOKEN_BEDROCK`, bearer). Disabled by default (the Ollama /
OpenRouter opt-in pattern).

This is the redo of #459's original Claude-only `InvokeModel` adapter,
rebuilt on the gateway-refactor seams (#481–#497). @depopry's SigV4
signer, AWS event-stream frame decoder, `BedrockAuth`, the `aws_sigv4`
credential grammar, `AdapterKind::Bedrock`, region-from-base_url, and
the lean-deps decision are preserved and authored by him on the first
two commits; the per-family `BedrockCodec` trait he wrote turned out to
be the crate-wide `Codec` seam in miniature, so the refactor promoted
exactly that shape. The original Claude-only description is preserved in
a comment below.

## What's here

- **`AdapterKind::Bedrock` × `CodecKind::BedrockConverse`** on the
route, plus the `aws_sigv4` credential source (no static secret — the
adapter signs at request time; `fabro-auth` stays AWS-free).
*(@depopry)*
- **SigV4 signer + AWS event-stream `FrameDecoder`** on the lean AWS
stack (no `aws-sdk-bedrockruntime`; transport stays on `fabro-http`).
Re-targeted at Converse's direct-JSON stream frames; the signer resolves
credentials per request. *(@depopry)*
- **`bedrock_converse` codec** — Converse envelope (`system[]`, typed
content blocks, `inferenceConfig`, `toolConfig`), prompt caching via
`cachePoint`, thinking-signature round-trip through `reasoningContent`,
usage mapped onto the disjoint `TokenCounts` buckets,
`provider_options.bedrock` passthrough. Plus the adapter shell and an
event-stream byte loop beside the transport's shared SSE loop.
- **Catalog**: `bedrock.toml` (Claude incl. Fable 5, Nova 2, Llama 4,
Mistral, DeepSeek, Kimi, GLM, MiniMax, Nemotron, gpt-oss — cross-region
inference-profile ids, per-model `billing_policy` so Claude bills
Anthropic-style) and a companion **`bedrock-openai`** provider for
GPT-5.5/5.4 over the `bedrock-mantle` Responses endpoint (pure config
over the existing `openai_responses` codec, zero new code).
- Secrets registry (`AWS_BEARER_TOKEN_BEDROCK`), gitleaks rules for both
Bedrock key formats, the `docs/integrations/bedrock` guide, and live e2e
tests.

## Live verification (confirmed end-to-end against a real AWS account)

Verified on a real Bedrock account (us-east-2, SigV4 + bearer):

- **SigV4 + Converse** — multiple families (Claude, Nova, DeepSeek, …)
via the full settings → catalog → route → adapter → codec path.
- **ConverseStream** — streaming deltas through the workflow engine.
- **Multi-turn tool use** — agent loop with tool calls round-tripping
(no-arg tools included).
- **Multi-model routing** — Claude + DeepSeek pinned in one run through
the single Converse codec.
- **mantle Responses** — `openai.gpt-5.5` answered via the
`bedrock-openai` provider (bearer auth).

The exercise caught and fixed several issues that unit tests (static
creds, mocked transports) could not — see the follow-up commits below.

## Follow-up fixes from live testing (commits on top of the foundation)

1. **Worker AWS env** — the workflow worker scrubs its env to an
allowlist, so SigV4 (which re-resolves from the ambient chain per
request) couldn't work through `fabro run`. The AWS credential-chain
inputs now cross into the worker.
2. **Vault bearer key** — Bedrock was the only key-based provider
missing a `vault:` credential ref, so `fabro secret set
AWS_BEARER_TOKEN_BEDROCK` silently didn't feed it. Now resolves env →
vault → SigV4.
3. **Converse tool-encoding hardening** — a no-arg tool call's
`toolUse.input` is now a `{}` object (Bedrock rejects null), and every
tool `inputSchema` gets a top-level `type: "object"` (strict families
like DeepSeek reject a typeless schema Claude tolerates).
4. **Nova output cap** — `amazon.nova-2-lite` max_output 65536 → 65535
(Bedrock's per-request limit).

Earlier fixes already folded into the foundation commits: the
`aws-config` sleep-impl (default chain panicked) and AWS error-body
decoding (top-level `message`/`Message`/`__type` → proper messages
instead of "Unknown error").

## Manual testing & setup

See `docs/integrations/bedrock` — now documents the non-obvious account
setup that live testing surfaced: the per-Region Anthropic use-case
approval, `aws-marketplace:Subscribe` for third-party models, the Fable
5 / Mythos-class data-sharing opt-in, and the bearer-vs-SigV4 precedence
override for running Converse + mantle side by side.

## Open decision / discussion

- **Model-id naming** — Bedrock rows use dotted ids mirroring Bedrock's
native inference-profile ids (`us.anthropic.claude-sonnet-4-6`,
`openai.gpt-5.5`), which also makes them the wire `api_id`. Third scheme
alongside bare ids and OpenRouter's `vendor/model` slashes. No collision
risk (enforced at catalog build). Open to a uniform scheme if preferred.
- **`BEDROCK_API_KEY` alias** — see the comment thread; the AWS console
hands some users `export BEDROCK_API_KEY=` while the SDK-standard var is
`AWS_BEARER_TOKEN_BEDROCK`. Question of whether to accept both.

## Deferred (named follow-ups)

- **`qwen.qwen3-coder-next`** — omitted pending a verified Bedrock
model/inference-profile id (its fabro id isn't a valid Bedrock
identifier; needs an explicit `api_id`). Re-add once confirmed via `aws
bedrock list-inference-profiles`.
- **Claude Mythos 5** — Anthropic-Messages-only on `bedrock-mantle`
(limited preview).
- **Converse structured output** (`response_format` rejected with a
clear error).
- **`reasoning_effort` on Converse rows** via
`additionalModelRequestFields` (the `bedrock-openai` GPT rows already
accept effort levels).
- **CountTokens** route (`count_input_tokens` returns `None`).

## Verification

`cargo nextest run --workspace`: green except the pre-existing
environment-dependent fabro-workflow failures (identical on main).
clippy `-D warnings` + pinned-nightly fmt clean. Codec unit tests +
adapter httpmock tests + frame-decoder/signer locks.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: Scott Werner <scott@sublayer.com>
Co-authored-by: Scott Werner <stwerner@vt.edu>
2026-06-16 11:46:49 -04:00

474 lines
18 KiB
Text

---
title: "Settings Configuration"
description: "Configure CLI and shared machine defaults with settings.toml"
---
Fabro loads machine defaults from `~/.fabro/settings.toml`. The file is optional. If it does not exist, Fabro falls back to built-in defaults.
The CLI and server each read the sections relevant to their own process. On a remote deployment, the CLI machine and the server machine each have their own `settings.toml`.
<Note>
Fabro only reads `settings.toml`. Older `cli.toml`, `user.toml`, and `server.toml` filenames are no longer part of the supported config surface.
</Note>
## File location
The default path is `~/.fabro/settings.toml`.
Use `fabro server start --config /path/to/settings.toml` if the server should read a different file.
## Schema version
Every Fabro config file must declare its schema version with a top-level `_version` key:
```toml title="settings.toml"
_version = 1
```
Files that omit `_version` are treated as version `1`. The legacy top-level `version` key is no longer accepted and raises a targeted rename hint.
## Who reads what
`settings.toml` uses the same schema as `.fabro/project.toml` and `workflow.toml`, but each process only reads the fields it understands. The top-level schema is strictly namespaced — the only allowed domains are `[project]`, `[workflow]`, `[run]`, `[llm]`, `[cli]`, and `[server]`.
| Scope | Examples |
|---|---|
| CLI-only | `[cli.target]`, `[cli.auth]`, `[cli.exec]`, `[cli.output]`, `[cli.updates]`, `[cli.logging]` |
| Shared run defaults | `[run.model]`, `[run.environment]`, `[environments.<slug>]`, `[run.checkpoint]`, `[run.inputs]`, `[run.prepare]`, `[run.pull_request]`, `[run.integrations.github.permissions]`, `[run.hooks]`, `[run.agent.mcps]` |
| Shared LLM catalog | `[llm.providers.<id>]`, `[llm.models.<id>]`, model limits, features, controls, and costs |
| Server-only | `[server.listen]`, `[server.api]`, `[server.web]`, `[server.auth]`, `[server.storage]`, `[server.artifacts]`, `[server.slatedb]`, `[server.scheduler]`, `[server.logging]`, `[server.integrations]` |
`[cli.*]` and `[server.*]` stanzas are owner-specific: they are only consumed from `~/.fabro/settings.toml` (plus process-local flags and env overrides). The same stanzas in `.fabro/project.toml` or `workflow.toml` remain schema-valid but runtime-inert.
See [Server Configuration](/administration/server-configuration) for the server-owned sections.
## Precedence
Shared layered domains (`[project]`, `[workflow]`, `[run]`) use this override order:
1. **CLI flags** — always win
2. **Environment overrides** — Fabro-defined override channels
3. **`workflow.toml`** — per-workflow overrides
4. **`.fabro/project.toml`** — project defaults
5. **`~/.fabro/settings.toml`** — machine defaults
6. **Built-in defaults**
Owner-specific domains (`[cli.*]`, `[server.*]`) use a narrower trust boundary — only CLI flags, env overrides, `~/.fabro/settings.toml`, and built-in defaults apply.
## Full example
```toml title="settings.toml"
_version = 1
[cli.target]
type = "http"
url = "https://fabro.example.com/api/v1"
[cli.exec]
prevent_idle_sleep = true
[cli.exec.model]
provider = "anthropic"
name = "claude-opus-4-6"
[cli.exec.agent]
permissions = "read-write"
[cli.output]
format = "text"
verbosity = "normal"
[cli.updates]
check = true
[cli.logging]
level = "info"
[llm.providers.proxy]
display_name = "Acme Gateway"
adapter = "openai_compatible"
base_url = "https://llm-gateway.example.com/v1"
aliases = ["gateway"]
[llm.providers.proxy.auth]
credentials = ["env:ACME_GATEWAY_API_KEY", "vault:ACME_GATEWAY_API_KEY"]
[llm.providers.proxy.extra_headers]
x-portkey-api-key = { env = "PORTKEY_API_KEY" }
x-portkey-config = { literal = "@bedrock-prod" }
[llm.models."team-code-large"]
provider = "proxy"
api_id = "provider-wire-model-name"
agent_profile = "anthropic"
display_name = "Team Code Large"
default = true
aliases = ["team-code"]
[llm.models."team-code-large".controls]
reasoning_effort = ["low", "medium", "high"]
speed = ["fast"]
[llm.models."team-code-large".costs]
input_cost_per_mtok = 1.50
output_cost_per_mtok = 8.00
[llm.models."team-code-large".costs.speed.fast]
input_cost_per_mtok = 3.00
output_cost_per_mtok = 16.00
[run.model]
name = "claude-sonnet-4-5"
[run.git.author]
name = "fabro-bot"
email = "fabro-bot@company.com"
[run.pull_request]
enabled = true
[run.integrations.github.permissions]
contents = "write"
pull_requests = "write"
[run.agent.mcps.filesystem]
type = "stdio"
command = ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
startup_timeout = "15s"
tool_timeout = "90s"
[run.agent.mcps.filesystem.env]
NODE_ENV = "production"
[run.agent.mcps.sentry]
type = "http"
url = "https://mcp.sentry.dev/mcp"
[run.agent.mcps.sentry.headers]
Authorization = "Bearer sk-xxx"
```
All fields are optional. Include only the sections and keys you want to override. A single file can still include both CLI and server sections when you run both processes on one machine, but explicit remote targets do not read remote server state from the local machine.
{/* generated:options */}
## `[cli.target]`
Connection info for commands that target a remote Fabro server.
```toml title="settings.toml"
[cli.target]
type = "http"
url = "https://fabro.example.com/api/v1"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `type` | `"http"` \| `"unix"` | None | Explicit transport selection. |
| `url` | string | None | Required for `type = "http"`; the API base URL. |
| `path` | string | None | Required for `type = "unix"`; the absolute Unix socket path. |
## `[llm.providers.<id>]`
Define or override an LLM provider. Provider IDs are strings, so custom
providers can be added when they use an adapter Fabro already supports.
```toml title="settings.toml"
[llm.providers.proxy]
display_name = "Acme Gateway"
adapter = "openai_compatible"
base_url = "https://llm-gateway.example.com/v1"
priority = 50
enabled = true
aliases = ["gateway"]
[llm.providers.proxy.auth]
credentials = ["env:ACME_GATEWAY_API_KEY", "vault:ACME_GATEWAY_API_KEY"]
[llm.providers.proxy.extra_headers]
x-portkey-api-key = { env = "PORTKEY_API_KEY" }
x-portkey-config = { literal = "@bedrock-prod" }
x-team-secret = { vault = "gateway_team_secret" }
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `display_name` | string | provider ID | Human-readable provider name. |
| `adapter` | string | built-in value | Adapter registry key, such as `"anthropic"`, `"openai"`, `"gemini"`, or `"openai_compatible"`. Required for new providers. |
| `agent_profile` | `"anthropic"` \| `"openai"` \| `"gemini"` | derived from `adapter` | Agent profile used for project memory, CLI/ACP command selection, and native session routing. Override only when a provider needs profile behavior different from its adapter. |
| `billing_policy` | `"openai"` \| `"anthropic"` \| `"gemini"` \| `"none"` | derived from `adapter` | Provider-owned billing algorithm for usage estimates. Override for exceptional providers such as local no-billing runtimes. |
| `base_url` | string | built-in value or adapter runtime default | Provider API base URL. Required for most custom OpenAI-compatible providers. |
| `auth` | table | omitted | API-key auth config. Omit the table entirely for providers that need no API key; any `extra_headers` are still attached. |
| `auth.credentials` | array<string> | required when `auth` present | Ordered credential refs. Accepted forms are `vault:<NAME>`, `env:<NAME>`, and `aws_sigv4` (sign requests from the AWS default credential chain — Bedrock). Literal secret strings are rejected. |
| `auth.header` | `"bearer"` or `{ custom = "Header-Name" }` | `"bearer"` | Primary API-key header policy. Omit when the provider uses a standard bearer token. |
| `extra_headers` | table | `{}` | Additional headers attached to provider requests. Values must be typed refs: `{ literal = "..." }`, `{ env = "NAME" }`, or `{ vault = "NAME" }`. |
| `priority` | integer | `0` | Higher-priority configured providers win default selection; ties use canonical provider ID. |
| `enabled` | boolean | `true` | Set `false` to disable a provider after lower-precedence layers define it. |
| `aliases` | array<string> | `[]` | Additional provider names accepted by model routing and fallback config. |
## `[llm.models.<id>]`
Define or override a model in the catalog. The table key is the canonical
model ID Fabro users reference; `api_id` is the model string sent to the
provider API.
```toml title="settings.toml"
[llm.models."team-code-large"]
provider = "proxy"
api_id = "provider-wire-model-name"
agent_profile = "anthropic"
display_name = "Team Code Large"
family = "team-code"
default = true
probe = true
enabled = true
aliases = ["team-code"]
estimated_output_tps = 80
[llm.models."team-code-large".limits]
context_window = 200000
max_output = 32000
[llm.models."team-code-large".features]
tools = true
vision = false
reasoning = true
reasoning_effort = "levels"
prompt_cache = true
[llm.models."team-code-large".controls]
reasoning_effort = ["low", "medium", "high"]
speed = ["fast"]
[llm.models."team-code-large".costs]
input_cost_per_mtok = 1.50
output_cost_per_mtok = 8.00
cache_input_cost_per_mtok = 0.30
[llm.models."team-code-large".costs.speed.fast]
input_cost_per_mtok = 3.00
output_cost_per_mtok = 16.00
cache_input_cost_per_mtok = 0.60
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `provider` | string | None | Provider ID this model belongs to. |
| `api_id` | string | model ID | Identifier sent to the provider API. |
| `agent_profile` | `"anthropic"` \| `"openai"` \| `"gemini"` | provider profile | Agent profile override for this model. Model overrides take precedence over provider overrides. |
| `billing_policy` | `"openai"` \| `"anthropic"` \| `"gemini"` \| `"none"` | provider policy | Billing algorithm override for this model — for models whose billing family differs from their provider's (e.g. Claude served through OpenRouter bills Anthropic-style cache reads/writes). |
| `display_name` | string | model ID | Human-readable model name. |
| `family` | string | model ID | Family label used for catalog display and matching. |
| `training` | string | None | Training data cutoff label. |
| `knowledge_cutoff` | string or TOML date | None | Public knowledge cutoff label; TOML dates normalize to `YYYY-MM-DD`. |
| `default` | boolean | `false` | Whether this is the provider default model. |
| `probe` | boolean | `false` | Whether this model should be preferred for provider connectivity probes. Set `false` in a higher-precedence layer to clear an inherited probe marker. |
| `enabled` | boolean | `true` | Set `false` to disable a model after lower-precedence layers define it. |
| `aliases` | array<string> | `[]` | Additional model names accepted by routing and fallback config. |
| `estimated_output_tps` | number | None | Estimated output tokens per second for catalog display and planning. |
## `[llm.models.<id>.limits]`
| Key | Type / values | Default | Description |
|---|---|---|---|
| `context_window` | integer | None | Maximum context window size in tokens. |
| `max_output` | integer | None | Maximum output tokens, if known. |
## `[llm.models.<id>.features]`
| Key | Type / values | Default | Description |
|---|---|---|---|
| `tools` | boolean | `false` | Whether the model supports tool calls. |
| `vision` | boolean | `false` | Whether the model accepts image inputs. |
| `reasoning` | boolean | `false` | Whether the model has reasoning behavior. |
| `reasoning_effort` | `"levels"` \| `"always_adaptive"` \| `"none"` | `"none"` | Whether the model endpoint supports a native reasoning-effort parameter. `levels` accepts discrete effort levels; `always_adaptive` accepts effort levels with natively always-on adaptive thinking; `none` has no native effort parameter. |
| `prompt_cache` | boolean | `false` | Whether prompt cache pricing/usage applies. |
| `sampling_params` | boolean | `true` | Whether the model accepts classic sampling parameters (`temperature`, `top_p`). |
## `[llm.models.<id>.controls]`
| Key | Type / values | Default | Description |
|---|---|---|---|
| `reasoning_effort` | array<string> | all standard levels when feature is `"levels"` or `"always_adaptive"` | User-facing reasoning effort values Fabro may send for this model. Can be set explicitly for reasoning models whose provider adapter maps effort to a non-native API shape. |
| `speed` | array<string> | `[]` | Additional speeds beyond implicit `standard`; do not list `standard`. |
## `[llm.models.<id>.costs]`
| Key | Type / values | Default | Description |
|---|---|---|---|
| `input_cost_per_mtok` | number | None | Input cost in USD per million tokens. |
| `output_cost_per_mtok` | number | None | Output cost in USD per million tokens. |
| `cache_input_cost_per_mtok` | number | None | Cached input/read cost in USD per million tokens. |
## `[llm.models.<id>.costs.speed.<speed>]`
Per-speed cost overrides use the same keys as `[llm.models.<id>.costs]`.
Each `<speed>` key must be declared in `[llm.models.<id>.controls].speed`.
The `standard` speed is implicit and always uses the base cost table.
## `[cli.updates]`
`[cli.updates]` — upgrade check toggle
```toml title="settings.toml"
[cli.updates]
check = true
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `check` | boolean | true | Check for new Fabro releases during supported CLI commands. |
## `[cli.output]`
`[cli.output]` — generic CLI output defaults
```toml title="settings.toml"
[cli.output]
format = "text"
verbosity = "verbose"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `format` | "text" \| "json" | "text" | Output format for commands that support machine-readable output. |
| `verbosity` | "quiet" \| "normal" \| "verbose" | "normal" | Default output verbosity. |
## `[cli.exec]`
`[cli.exec]` — `fabro exec` defaults
```toml title="settings.toml"
[cli.exec]
prevent_idle_sleep = true
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `prevent_idle_sleep` | boolean | false | Prevent idle sleep on macOS while an exec run is in flight. |
## `[cli.exec.model]`
```toml title="settings.toml"
[cli.exec.model]
provider = "anthropic"
name = "claude-opus-4-6"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `name` | string | None | Model name for `fabro exec`. |
| `provider` | string | None | LLM provider for `fabro exec`. |
## `[cli.exec.agent]`
```toml title="settings.toml"
[cli.exec.agent]
permissions = "read-write"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `mcps` | table | None | Agent-scoped MCP entries for `fabro exec`. |
| `permissions` | "read-only" \| "read-write" \| "full" | "read-write" | Tool permission level for `fabro exec`. |
## `[run.model]`
`[run.model]` — provider-neutral default model selection
```toml title="settings.toml"
[run.model]
provider = "anthropic"
name = "claude-sonnet-4-5"
fallbacks = ["openai", "gpt-5.4"]
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `fallbacks` | array<string> | [] | Ordered list of fallback model references. Supports `...` splice marker<br />at layering time — see [`super::splice_array`]. |
| `name` | string | None | Model name for workflow runs. |
| `provider` | string | None | Provider name for workflow model selection. |
## `[cli.logging]`
`[cli.logging]` — process-owned logging configuration for the CLI
```toml title="settings.toml"
[cli.logging]
level = "info"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `level` | "error" \| "warn" \| "info" \| "debug" \| "trace" | "info" | Default CLI log level. |
## `[run.git.author]`
```toml title="settings.toml"
[run.git.author]
name = "fabro-bot"
email = "fabro-bot@company.com"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `email` | string | "fabro@local" | Git author email for checkpoint commits. |
| `name` | string | "fabro" | Git author name for checkpoint commits. |
## `[run.pull_request]`
`[run.pull_request]` — provider-neutral PR behavior
```toml title="settings.toml"
[run.pull_request]
enabled = true
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `auto_merge` | boolean | false | Enable GitHub auto-merge for created pull requests. Implies `draft =<br />false`. |
| `draft` | boolean | true | Open created pull requests as drafts. |
| `enabled` | boolean | false | Automatically create a PR after successful runs. |
| `merge_strategy` | "merge" \| "squash" \| "rebase" | "squash" | Merge method to configure for the pull request. |
## `[run.agent]`
`[run.agent]` — agent knobs only (Fabro tools, permissions, MCPs)
```toml title="settings.toml"
[run.agent]
fabro_tools = true
permissions = "read-write"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `fabro_tools` | boolean | false | Allow workflow agents to use Fabro run-management tools. |
| `mcps` | table | None | Agent-scoped MCP server entries, keyed by name. |
| `permissions` | "read-only" \| "read-write" \| "full" | "read-write" | Default tool permission level for workflow agents. |
## `[run.agent.mcps.<name>]`
Configure MCP servers for workflow agents. For `fabro exec`-only MCPs, use `[cli.exec.agent.mcps.<name>]` with the same shape.
```toml title="settings.toml"
[run.agent.mcps.filesystem]
type = "stdio"
command = ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/workspace"]
startup_timeout = "15s"
tool_timeout = "90s"
```
| Key | Type / values | Default | Description |
|---|---|---|---|
| `type` | `"stdio"` \| `"http"` \| `"sandbox"` | None | MCP transport type. |
| `command` | array<string> | None | Command and arguments for `stdio` or `sandbox` transports. |
| `script` | string | None | Shell script alternative to `command` for process-launching transports. |
| `url` | string | None | Remote MCP URL for `http` transport. |
| `port` | integer | None | Sandbox port for `sandbox` transport. |
| `env` | table | `{}` | Additional environment variables for process-launching transports. |
| `headers` | table | `{}` | HTTP headers for `http` transport. |
| `startup_timeout` | duration | `"10s"` | Max duration for startup and MCP handshake. |
| `tool_timeout` | duration | `"60s"` | Max duration for a single MCP tool call. |
See [MCP](/agents/mcp) for transport-specific examples.
{/* /generated:options */}