Mcp Dispatch

# MCP Dispatch Filter
# Requires `--features openai-mcp-tools,store-sqlite` because these filters are opt-in.
#
# Demonstrates the `openai_mcp_dispatch` filter configuration. The
# filter executes MCP tool calls against upstream MCP servers within
# the Responses API agentic loop. It identifies MCP tool calls from
# the model response, checks approval policies, calls `tools/call`
# on the originating server, and appends the results.
#
# Requires `openai_agentic_loop` (#26) for end-to-end execution. Without
# it the filter validates its configuration at startup but cannot
# be triggered at runtime.
#
# Pipeline ordering:
#   tool_parse → mcp_tool_resolve → mcp_dispatch
#
# `openai_stream_events` is not part of this minimal dispatch demo: it
# composes streamed inference rounds into one logical Responses stream and
# is only valid inside an `iterative_request_router` step. See
# `agentic-loop.yaml` for the end-to-end streaming loop that pairs
# `openai_mcp_dispatch` with `openai_stream_events` inside IRR.
#
# Configuration:
#   timeout_ms:      Per-call timeout for tools/call (default: 30000)
#
# The `tools/call` callout is dialed through the pipeline's sub-request
# executor; the MCP transport stages the SSRF-validated dial target itself. This
# filter is meant to run inside the `iterative_request_router` step pipeline (the
# per-round executor). praxis core builds each IRR step with a live chain-binding
# context, so `openai_mcp_dispatch` binds an **inline** `outbound_chain`; it
# rejects a **named** reference, because IRR supplies each step an empty
# top-level named-chain map and a named reference cannot resolve there.
# `openai_mcp_tool_resolve`, a top-level filter, additionally accepts a named
# reference — see mcp-outbound-chain.yaml.
#
# SSRF posture: the outbound MCP callout is validated against the pipeline's
# `insecure_options.allow_private_upstreams` (default: false). There is no
# per-filter loopback opt-in; a loopback MCP server requires
# `allow_private_upstreams: true` globally.

listeners:
  - name: ai-gateway
    address: "127.0.0.1:8080"
    filter_chains: [mcp-dispatch-pipeline]

filter_chains:
  - name: mcp-dispatch-pipeline
    filters:
      - filter: openai_tool_parse
      - filter: openai_mcp_tool_resolve
        timeout_ms: 5000
      - filter: openai_mcp_dispatch
        timeout_ms: 30000
      - filter: router
        routes:
          - path_prefix: "/"
            cluster: "inference"
      - filter: load_balancer
        clusters:
          - name: "inference"
            endpoints:
              - "127.0.0.1:3001"

insecure_options:
  allow_private_endpoints: true # example proxies to local backends