Stéphane Tailland
67d7bffd1c
Design agreed in discussion, nothing built yet. Records the agent's role, its scopes and MCP servers, its A2A surface, and the framework choice with its reason — so the next session starts from the decisions rather than re-deriving them. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
56 lines
2.1 KiB
Markdown
56 lines
2.1 KiB
Markdown
# Catalogue — product management agent
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**Framework:** PydanticAI + FastA2A · **Mode:** conversational · **Client id:** `classic-models-agent-catalogue`
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Serves the product manager: create and update products and product lines, set
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prices, decide what to discontinue.
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## Scopes and MCP servers
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| MCP | catalog (`mcp-catalog-audience`), sales (`mcp-sales-audience`) |
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| Scopes | `catalog:read`, `catalog:write`, `sales:read` |
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`sales:read` is there to answer "does this product actually sell?" before a
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pricing or discontinuation decision. Read only — this agent never touches orders.
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## A2A
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**Calls** — `Approvisionnement.check_availability`, to know remaining stock and
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open orders before discontinuing a reference.
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**Exposes** — nothing yet. Add skills only when another agent needs them.
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## Why PydanticAI here
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Not arbitrary. This is the agent that **writes structured data under
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constraints** — prices, quantities, references, product line membership. Typed
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validation of the model's output earns its place here in a way it would not on a
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purely conversational agent.
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## FastA2A, not to_a2a()
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Use FastA2A directly. `Agent.to_a2a()` still works in PydanticAI 1.x but emits
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a deprecation warning and is removed in v2.
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FastA2A is framework-agnostic, so whatever is built here is also the natural
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basis for the A2A adapter in front of the n8n agents — see
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`classic-models-agent-flows`.
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## Before writing code
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Read `classic-models-agent-platform/CLAUDE.md` first. It holds the identity
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model, the MCP and LLM endpoints, the token-exchange recipe and the known
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blockers. This file only covers what is specific to this agent.
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## Non-negotiables
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- **Inference goes through the Kong AI gateway**, `https://llm-dev.sttlab.local/llm/v1/chat/completions`.
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Calling a provider directly bypasses the rate limits and prompt guards.
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- **One MCP client per conversation turn**, built with freshly exchanged tokens.
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Do not hold a long-lived MCP connection with a fixed token.
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- **A2A calls return answers, not data.** Expose narrow decision-shaped skills,
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never a generic `query`.
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- Deliverables in English.
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