Agent Fleet MCP Server
MCP server that exposes a pool of Claude SDK agents as tools, enabling deterministic agent creation, retrieval, and resumption from a capability corpus.
README
agent-fleet
Assembles a minimal Claude Agent SDK agent from a problem statement and a capability corpus, then runs and resumes it. The generation path is deterministic — no LLM and no network in it, so the same request and corpus produce a byte-identical agent. Running the agent is the part that talks to the SDK.
Repository layout
Python only. One PyPI package (claude-sdk-agent-fleet), three subpackages layered so each depends on the ones
below it. pip install claude-sdk-agent-fleet gets the core engine and MCP pool server;
claude-sdk-agent-fleet[api] (or [all]) adds the FastAPI service.
| Path | What it is | Run or import | Install |
|---|---|---|---|
src/agent_fleet/ |
core engine: pipeline, router, pool | imported | claude-sdk-agent-fleet |
src/agent_fleet_api/ |
FastAPI service over the core | run | claude-sdk-agent-fleet[api] |
src/agent_fleet_mcp/ |
MCP server exposing the pool as tools (pool-mcp) |
run | claude-sdk-agent-fleet |
agent_fleet_api ─┐
├─imports──▶ agent_fleet ──imports──▶ capdisc
agent_fleet_mcp ─┘
The API front-end carries the web dependencies as an optional extra so the core engine carries none by default. Environment scanning lives in capdisc, a separate, public repo consumed as a pinned git dependency.
Pipeline
ProblemRequest ─▶ recall ─▶ select ─▶ compose ─▶ score ─▶ render
(source) (budget) (AgentSpec) (efficiency) (SDK program)
- recall — a
CatalogSourceranks the corpus by lexical relevance (a pluggableRanker) and trims to a limit. - select — keep candidates above a relevance threshold (plus pinned), capped by tool/skill budgets.
- compose — map the selected refs into an
AgentSpecwith a templated system prompt. - score — check the spec against tool/skill/prompt budgets (
efficiency). - render — emit a runnable Claude Agent SDK program.
Pool
AgentPool (SQLite) keys each pooled agent by a stable AgentKey and stores the AgentSpec and
session id that built it, so a run can be retrieved, resumed against the same live SDK
conversation, or found fuzzily. run_with_capture observes the live message stream to record the
real, resumable session id of every agent a run involves — the top-level agent and each dispatched
subagent. Runs, per-agent runs, and findings are persisted alongside the entry.
Develop
uv sync --extra api
uv run pytest
uv run ruff check
uv run mypy src
make coverage # test coverage, printed to the terminal
Details: docs/OVERVIEW.md · docs/pipeline.md · docs/catalog-boundary.md
Coverage
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