Quick Start

A strategy is three files. Two of them — contract.yaml and strategy.py — are the publishable, environment-agnostic artifact. The third — binding.yaml — is what binds the strategy to your environment. Two teams can share a strategy by sharing the first two files; each team writes its own binding to point at its own MCP backends. This walkthrough builds a strategy that counts documents in an Elasticsearch index.

The directory

strategies/
  security/
    enrichment/
      my_strategy/
        contract.yaml      # what data the strategy needs
        strategy.py        # the DAG of @step methods
        binding.yaml       # how to fetch it in *this* environment

The convention is strategies/<domain>/<category>/<slug>/ — domain first (e.g. security, infra), then category (e.g. enrichment, hunt, detection). The runner discovers strategies regardless of nesting depth.

contract.yaml

Declares the strategy’s identity, parameters, and the abstract data tables it needs:

name: "my-strategy"
version: "1.0.0"
description: "Count documents in a staged table."
tags: [enrichment, example]
params:
  limit:
    type: int
    required: false
    default: 100
    description: "Max rows to scan"
requires:
  graph: false
  tables:
    my_data:
      description: "Input data from Elasticsearch"
      optional: false
      columns:
        _id:
          type: VARCHAR
        message:
          type: VARCHAR

Note that the contract talks about a table called my_data with two columns. It says nothing about where that table comes from — that’s the binding’s job.

strategy.py

The DAG of pipeline steps. Each @step reads from ctx.duckdb (where staged tables are pre-loaded) and returns a value that downstream steps can depend on by parameter name:

from fracta_strategies import Strategy, step

class MyStrategy(Strategy):

    @step("Load data")
    def load_data(self, ctx):
        rows = ctx.duckdb.execute(
            "SELECT _id, message FROM my_data LIMIT ?",
            [ctx.params.get("limit", 100)]
        ).fetchall()
        return [{"id": r[0], "message": r[1]} for r in rows]

    @step("Summarize")
    def summarize(self, ctx, load_data):
        return {
            "count": len(load_data),
            "sample": load_data[:3],
        }

contract.yaml and strategy.py together are the part of the strategy you can publish, share, and version-control without leaking anything about your specific environment.

binding.yaml

The binding maps the contract’s abstract my_data table to a concrete MCP data source — in this case, an Elasticsearch index reachable through the gateway:

source_bindings:
  my_data:
    backend: elasticsearch
    config_key: elastic
    fetch_mode: fracta_mcp_gateway
    mcp_tool: search
    mcp_server: elastic
    index: "logs-*"
    field_map:
      _id: _id
      message: message

A different environment would write a different binding — different backend, different index, different field names — and the same contract.yaml + strategy.py would run unchanged. See Portability for the full story on why this split exists, and Bindings for the complete schema and fetch modes.

Run it

An agent calls strategy_run(name="my-strategy"). The gateway reads the binding to know where to fetch each table declared in the contract, stages the result as Parquet, loads it into a per-run DuckDB, and the sidecar executes the steps in DAG order. The agent gets back the structured summarize result.

A complete worked example in the repo

For a runnable three-file strategy you can copy and adapt, see strategies/_example/security/enrichment/splunk_field_survey/ in the repo. It surveys Splunk indexes and the fields extracted per sourcetype, and ships with a reference binding.yaml you can edit to point at your own Splunk MCP server. The _example/ directory is skipped by the runner (directories starting with _ are ignored on discovery) — copy a strategy out into a real <domain>/<category>/ path under strategies/ to make it live.

Strategies overview — the full framework reference
Contracts — every field in contract.yaml
Bindings — every field in binding.yaml, plus fetch modes and pagination
Portability — why the contract/binding split exists
Lifecycle — discovery, hot reload, and governance status

Introduction

Getting Started

Swarm

Strategies

Patterns

Guides

Reference

Operations

Development

Contributing

The directory

contract.yaml

strategy.py

binding.yaml

Run it

A complete worked example in the repo

Next

​The directory

​contract.yaml

​strategy.py

​binding.yaml

​Run it

​A complete worked example in the repo

​Next

The directory

contract.yaml

strategy.py

binding.yaml

Run it

A complete worked example in the repo

Next